|Número de publicación||USRE44966 E1|
|Tipo de publicación||Concesión|
|Número de solicitud||US 13/284,254|
|Fecha de publicación||24 Jun 2014|
|Fecha de presentación||28 Oct 2011|
|Fecha de prioridad||28 Nov 2003|
|Número de publicación||13284254, 284254, US RE44966 E1, US RE44966E1, US-E1-RE44966, USRE44966 E1, USRE44966E1|
|Inventores||Steven Dennis Flinn, Naomi Felina Moneypenny|
|Cesionario original||World Assets Consulting Ag, Llc|
|Exportar cita||BiBTeX, EndNote, RefMan|
|Citas de patentes (191), Otras citas (76), Citada por (15), Clasificaciones (2), Eventos legales (3)|
|Enlaces externos: USPTO, Cesión de USPTO, Espacenet|
The present application claims priority under 35 U.S.C. § 119 to PCT International Application No. PCT/US2004/037176, which claimed priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 60/525,120, entitled “A Method and System for Adaptive Fuzzy Networks,” filed Nov. 28, 2003. The present application is a reissue application of U.S. patent application Ser. No. 11/419,547, filed May 22, 2006, now U.S. Pat. No. 7,526,458, issued Apr. 28, 2009, entitled ADAPTIVE RECOMMENDATIONS SYSTEM; which is a continuation of PCT International Application No. PCT/US2004/037176, entitled ADAPTIVE RECOMBINANT SYSTEMS, filed Nov. 4, 2004, now expired; which claims the benefit of U.S. Provisional Patent Application Ser. No. 60/525,120, entitled A METHOD AND SYSTEM FOR ADAPTIVE FUZZY NETWORKS filed Nov. 28, 2003.
This invention relates to software programs that adapt according to their use over time, and that may be distributed and recombined as a whole or in part across one or more computer systems.
Current general purpose computer-based information management approaches include flat files, hypertext models (e.g., World Wide Web), and relational database management systems (RDBMS). A fundamental problem with all of these approaches is “brittleness”—they have limited inherent ability to adapt to changing circumstances without direct human intervention. For the more robust of these information management approaches (e.g., relational database management system, or RDBMS), the human intervention may be somewhat reduced compared to that of less sophisticated approaches (e.g., flat files), but the need for direct, manual effort is certainly not eliminated.
Likewise, specific computer applications that are underpinned by the prior art information management approaches are generally very limited in their ability to adapt to changing circumstances and user requirements over time. In addition to prior art information management approaches and the computing applications built on them generally being too brittle, they also can be criticized for being monolithic—that is, it is generally not possible to dynamically separate subsets of a computing application and recombine them with other subsets of a plurality of computing applications to form new and useful applications. In other words, prior art computing systems and applications are very limited in their ability to usefully evolve without directed human programming or content management attention. This is a significant root cause of the well-known and well-discussed “software bottleneck.”
An adaptive recombinant system is disclosed to address the problems of limited adaptation and extensibility associated with prior art computing applications by incorporating an information management and computing system paradigm that has built-in capabilities to facilitate adaptation to changing circumstances and user requirements and preferences. The adaptive recombinant system can track, store and make user preference and interest inferences from a broad array of system usage behaviors. These inferencing capabilities may be applied to not only assist system users in more effectively navigating the system, but may also be applied to modify system structure and content so as to embed adaptation directly within the system and content, thereby enabling the system to evolve to become ever more effective over time.
Furthermore, users of the system may themselves be represented or explicitly referenced within system content. Fundamentally, the adaptive recombinant system represents a computer-based systems architecture in which system users may be represented directly within the system content and structure, and the usage behaviors over time of the users may be embedded directly in the system structure. Thus, the adaptive recombinant system explicitly integrates the system, users of the system, and usage of the system in a way that extends beyond the less integrative, and more ad hoc approaches of prior art; thereby enabling a higher degree of computer-based system adaptiveness and extensibility. The adaptive recombinant system can complement current information management and computer application approaches to enable the resulting overall system to be more adaptive to individual and community user requirements.
In some embodiments, a network (where the term “network” is used as a term denoting a general system topology, not to be confused with specific application or use of the term, such as, for example, a “telecommunications network”) system structure is employed to facilitate adequate structural plasticity to enable system adaptation, and to enable syndication and combinations of system subsets. The network-based system structure may furthermore be based on a fuzzy network or fuzzy content network architecture.
In accordance with the embodiments described herein, an adaptive system, an adaptive recombinant system, and methods for establishing the systems are disclosed. The adaptive system includes algorithms for tracking user interactions with a collection of system objects, and generates adaptive recommendations based on the usage behaviors associated with the system objects. The adaptive recommendations may be explicitly represented to the user or may be used to automatically update the collection of system objects and associated relationships. In either case, the collection of objects and associated relationships become more useful to the user over time.
The adaptive recombinant system, which includes the adaptive system, may further be syndicated to other computer applications, including other adaptive systems. The adaptive recombinant system may recombine and resyndicate indefinitely. Both the adaptive system and the adaptive recombinant system may be based on a fuzzy network or a fuzzy content network structure.
The adaptive system may be implemented on a single computer or on multiple computers that are connected through a network, such as the Internet. The software and data storage associated with the adaptive system may reside on the single computer, or may be distributed across the multiple computers. The adaptive system may be implemented on stationary computers, on mobile computing devices, on processing units architected according to Von Neumann designs, or on those designed according to non-Von Neumann architectures. The adaptive system may integrate with existing types of computer software, such as computer operating systems, including mobile device operating systems and special purpose devices, such as television “set-top boxes,” network operating systems, database software, application middleware, and application software, such as enterprise resource planning (ERP) applications, desktop productivity tools, Internet applications, and so on.
In the following description, numerous details are set forth to provide an understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these details and that numerous variations or modifications from the described embodiments may be possible.
As used herein, one or more users 200 may be a single user or multiple users. As shown in
A user 200 may be a human entity, a computer system, or a second adaptive system (distinct from the adaptive system 100) that interacts with, or otherwise uses the adaptive system. The one or more users 200 may include non-human users of the adaptive system 100. In particular, one or more other adaptive systems may serve as virtual system “users.” These other adaptive systems may operate in accordance with the architecture of the adaptive system 100. Thus, multiple adaptive systems may be mutual users for one another.
It should be understood that the structural aspect 210, the content aspect 230, the usage aspect 220, and the recommendations function 240 of the adaptive system 100, and elements of each, may be contained within one computer, or distributed among multiple computers. Furthermore, one or more non-adaptive systems 258 may be modified to become one or more adaptive systems 100 by integrating the usage aspect 220 and the recommendations function 240 with the one or more non-adaptive systems 258.
The term “computer system” or the term “system,” without further qualification, as used herein, will be understood to mean either a non-adaptive or an adaptive system. Likewise, the terms “system structure” or “system content,” as used herein, will be understood to refer to the structural aspect 210 and the content aspect 230, respectively, whether associated with the non-adaptive system 258 or the adaptive system 100. The term “system structural subset” or “structural subset,” as used herein, will be understood to mean a portion or subset of the structural aspect 210 of a system.
The structural aspect 210 of the adaptive system 100 is depicted in the block diagram of
The objects 212 may be managed in a relational database, or may be maintained in structures such as flat files, linked lists, inverted lists, hypertext networks, or object-oriented databases. The objects 212 may include meta-information 234 associated with the information 232 contained within, or referenced by the objects 212.
As an example, in some embodiments, the World-wide Web could be considered a structural aspect, where web pages constitute the objects of the structural aspect and links between web pages constitute the relationships among the objects. Alternatively, or in addition, in some embodiments, the structural aspect could be comprised of objects associated with an object-oriented programming language, and the relationships between the objects associated with the protocols and methods associated with interaction and communication among the objects in accordance with the object-oriented programming language.
The one or more users 200 of the adaptive system 100 may be explicitly represented as objects 212 within the system 100, therefore becoming directly incorporated within the structural aspect 210. The relationships among objects 214 may be arranged in a hierarchical structure, a relational structure (e.g. according to a relational database structure), or according to a network structure.
The content aspect 230 of the adaptive system 100 is depicted in the block diagram of
The content aspect 230 may be updated based on the usage aspect 220, as well as associated metrics. To achieve this, the adaptive system 100 may employ the usage aspect of other systems. Such systems may include, but are not limited to, other computer systems, other networks, such as the World Wide Web, multiple computers within an organization, other adaptive systems, or other adaptive recombinant systems. In this manner, the content aspect 230 benefits from usage occurring in other environments.
The usage aspect 220 of the adaptive system 100 is depicted in the block diagram of
The captured usage information 202, known also as system usage or system use 202, includes any interaction by the one or more users 200 with the system. The adaptive system 100 tracks and stores user key strokes and mouse clicks, for example, as well as the time period in which these interactions occurred (e.g., timestamps), as captured usage information 202. From this captured usage information 202, the adaptive system 100 identifies usage behaviors 270 of the one or more users 200 (e.g., web page access or email transmission). Finally, the usage aspect 220 includes usage-behavior pre-processing, in which usage behavior categories 246, usage behavior clusters 247, and usage behavioral patterns 248 are formulated for subsequent processing of the usage behaviors 270 by the adaptive system 100. Some usage behaviors 270 identified by the adaptive system 100, as well as usage behavior categories 246 designated by the adaptive system 100, are listed in Table 1, and described in more detail, below.
The usage behavior categories 246, usage behaviors clusters 247, and usage behavior patterns 248 may be interpreted with respect to a single user 200, or to multiple users 200, in which the multiple users may be described herein as a community, an affinity group, or a user segment. These terms are used interchangeably herein. A community is a collection of one or more users, and may include what is commonly referred to as a “community of interest.” A sub-community is also a collection of one or more users, in which members of the sub-community include a portion of the users in a previously defined community. Communities, affinity groups, and user segments are described in more detail, below.
Usage behavior categories 246 include types of usage behaviors 270, such as accesses, referrals to other users, collaboration with other users, and so on. These categories and more are included in Table 1, below. Usage behavior clusters 247 are groupings of one or more usage behaviors 270, either within a particular usage behavior category 246 or across two or more usage categories. The usage behavior pre-processing 204 may also determine new “clusterings” of user behaviors 270 in previously undefined usage behavior categories 246, across categories, or among new communities. Usage behavior patterns 248, also known as “usage behavioral patterns” or “behavioral patterns,” are also groupings of usage behaviors 270 across usage behavior categories 246. Usage behavior patterns 248 are generated from one or more filtered clusters of captured usage information 202.
The usage behavior patterns 248 may also capture and organize captured usage information 202 to retain temporal information associated with usage behaviors 270. Such temporal information may include the duration or timing of the usage behaviors 270, such as those associated with reading or writing of written or graphical material, oral communications, including listening and talking, or physical location of the user 200. The usage behavioral patterns 248 may include segmentations and categorizations of usage behaviors 270 corresponding to a single user of the one or more users 200 or according to multiple users 200 (e.g., communities or affinity groups). The communities or affinity groups may be previously established, or may be generated during usage behavior pre-processing 204 based on inferred usage behavior affinities or clustering. Usage behaviors 270 may also be derived from the use or explicit preferences 252 associated with other adaptive or non-adaptive systems.
Adaptive Recommendations Function
The term “recommendations” associated with the adaptive recommendations function 240 is used broadly in the adaptive system 100. The adaptive recommendations 250 may be displayed to a recommendations recipient. As used herein, a recommendations recipient is an entity who receives the adaptive recommendations 250. Thus, the recommendations recipient may include the one or more users 200 of the adaptive system 100, as indicated by the dotted arrow 255 in
Preferably, the adaptive system 100 identifies the preferences of the user 200 and adapts the adaptive system 100 in view of the preferences. Preferences describe the likes, tastes, partiality, and/or predilection of the user 200 that may be inferred during access of the objects 212 of the adaptive system 100. In general, user preferences exist consciously or sub-consciously within the mind of the user. Since the adaptive system 100 has no direct access to these preferences, they are generally inferred by the preference inferencing algorithm 242 of the adaptive recommendations function 240.
The preference inferencing algorithm 242, infers preferences based on information that may be obtained as the user 200 accesses the adaptive system 100. The preference inferencing algorithm and associated output 242 is also described herein generally as “preference inferencing” or “preference inferences” of the adaptive system 100. The preference inferencing algorithm 242 identifies three types of preferences: explicit preferences 252, inferred preferences 253, and inferred interests 254. Unless otherwise stated, the use of the term “preferences” herein is meant to include any or all of the elements 252, 253, and 254 depicted in
As used herein, explicit preferences 252 describe explicit choices or designations made by the user 200 during use of the adaptive system 100. The explicit preferences 252 may be considered to more explicitly reveal preferences than inferences associated with other types of usage behaviors. A response to a survey is one example where explicit preferences 252 may be identified by the adaptive system 100.
Inferred preferences 253 describe preferences of the user 200 that are based on usage behavioral patterns 248. Inferred preferences 253 are derived from signals and cues made by the user 200. (The derivation of inferred preferences 253 by the adaptive system 100 is included in the description of
Inferred interests 254 describe interests of the user 200 that are based on usage behavioral patterns 248. In general, the adaptive recommendations 250 produced by the preference inferencing algorithm 242 combine inferences from overall user community behaviors and preferences, inferences from sub-community or expert behaviors and preferences, and inferences from personal user behaviors and preferences. As used herein, preferences (whether explicit 252 or inferred 253) are distinguishable from interests (254) in that preferences imply a ranking (e.g., object A is better than object B) while interests do not necessarily imply a ranking.
A second algorithm 244, designated recommendations optimization 244, optimizes the adaptive recommendations 250 produced by the adaptive system 100. The adaptive recommendations 250 may be augmented by automated inferences and interpretations about the content within individual and sets of objects 232 using statistical pattern matching of words, phrases or representations, in written or audio format, or in pictorial format, within the content. Such statistical pattern matching may include, but is not limited to, semantic network techniques, Bayesian analytical techniques, neural network-based techniques, support vector machine-based techniques, or other statistical analytical techniques. Relevant statistical techniques that may be applied by the present invention include those found in Vapnik, The Nature of Statistical Learning Theory, 1999.
As shown in
The adaptive recommendations 250 are presented as structural subsets of the structural aspect 210.
Three structural subsets 280A, 280B, and 280C (collectively, structural subsets 280) are depicted. The structural subset 280A includes three objects 212 and one associated relationship, which are reproduced by the adaptive recommendations function 240 in the same form as in the structural aspect 210 (objects are speckle shaded). The structural subset 280B includes a single object (object is shaded), with no associated relationships (even though the object originally had a relationship to another object in the structural aspect 210).
The third structural subset 210C includes five objects (striped shading), but the relationships between objects has been changed from their orientation in the structural aspect 210. In the structural subset 280C, a relationship 282 has been eliminated while a new relationship 284 has been formed by the adaptive recommendations function 240. The structural subsets 280 depicted in
The illustration in
The adaptive recommendations 250 may be in the context of a currently conducted activity of the system 100, a currently accessed object 232, or a communication with another user 200. The adaptive recommendations 250 may also be in the context of a historical path of executed system activities, accessed objects 212, or communications during a specific user session or across user sessions. The adaptive recommendations 250 may be without context of a current activity, currently accessed object 212, current session path, or historical session paths. Adaptive recommendations 250 may also be generated in response to direct user requests or queries. Such user requests may be in the context of a current system navigation, access or activity, or may be outside of any such context.
Usage Behavior Categories
In Table 1, several different usage behaviors 270 identified by the adaptive system 100 are categorized. The usage behaviors 270 may be associated with the entire user community, one or more sub-communities, or with individual users of the adaptive system 100.
Usage behavior categories and usage behaviors.
usage behavior category
navigation and access
activity, content and computer application
accesses, including buying/selling paths of
accesses or click streams
personal or community subscriptions to
process topical areas
interest and preference self-profiling
affiliation self-profiling (e.g., job function)
referral to others
discussion forum activity
direct communications (voice call, messaging)
content contributions or structural alterations
personal or community storage and tagging
personal or community organizing of stored or
user ratings of activities, content, computer
applications and automatic recommendations
location over time
relative location to users/object references
A first category of usage behaviors 270 is known as system navigation and access behaviors. System navigation and access behaviors include usage behaviors 270 such as accesses to, and interactions with, objects 212, such as activities, content, topical areas, and computer applications. These usage behaviors may be conducted through use of a keyboard, a mouse, oral commands, or using any other input device. Usage behaviors 270 in the system navigation and access behaviors category may include, but are not limited to, the viewing or reading of displayed information, typing written information, interacting with online objects orally, or combinations of these forms of interactions with the adaptive system 100.
System navigation and access behaviors may also include executing transactions, including commercial transactions, such as the buying or selling of merchandise, services, or financial instruments. System navigation and access behaviors may include not only individual accesses and interactions, but the capture and categorization of sequences of object accesses and interactions over time.
A second category of usage behaviors 270 is known as subscription and self-profiling behaviors. Subscriptions may be associated with specific topical areas of the adaptive system 100, or may be associated with any other structural subset 280 of the system 100. Subscriptions may thus indicate the intensity of interest (inferred interests 254) with regard to system objects 212, including specific topical areas. The delivery of information to fulfill subscriptions may occur online, such as through electronic mail (email), on-line newsletters, XML feeds, etc., or through physical delivery of media.
Self-profiling refers to other direct, persistent (unless explicitly changed by the user) indications explicitly designated by the one or more users 200 regarding their preferences and interests, or other meaningful attributes. The user 200 may explicitly identify interests or affiliations, such as job function, profession, or organization, and preferences, such as representative skill level (e.g., novice, business user, advanced). Self-profiling enables the adaptive system 100 to infer explicit preferences 252. For example, a self-profile may contain information on skill levels or relative proficiency in a subject area, organizational affiliation, or a position held in an organization. Self-profiling information may be used to infer preferences and interests with regard to system use and associated topical areas, and with regard to degree of affinity with other user community subsets. The user 200 may identify preferred methods of information receipt or learning style, such as visual or audio, as well as relative interest levels in other communities.
A third category of usage behaviors 270 is known as collaborative behaviors. Collaborative behaviors are interactions among the one or more users 200 of the adaptive system 100, or between users 200 and non-system users. Collaborative behaviors may thus provide information on areas of interest and intensity of interest. Interactions including online referrals of objects 212, such as through email, or structural subsets 280 of the system 100, whether to other system users 200 or to non-users 260, are types of collaborative behaviors obtained by the adaptive system 100.
Other examples of collaborative behaviors include, but are not limited to, online discussion forum activity, contributions of content or other types of objects 212 to the structural aspect 210 of the adaptive system 100, or any other alterations of the structural aspect 210 for the benefit of others. Collaborative behaviors may also include general user-to-user communications, whether synchronous or asynchronous, such as email, instant messaging, interactive audio communications, and discussion forums, as well as other user-to-user communications that can be tracked by the adaptive system 100.
A fourth category of usage behaviors 270 is known as reference behaviors. Reference behaviors refer to the saving or tagging of specific objects 212 or structural subsets 280 of the system 100 by the user 200 for recollection or retrieval at a subsequent time. The saved or tagged objects 212, or structural subsets 280, may be organized in a manner customizable by the user 200. The referenced objects 212 (structural subsets 280), as well as the manner in which they are organized by the user 200, may provide information on inferred interests 254 and intensity of interest.
A fifth category of usage behaviors 270 is known as direct feedback behaviors. Direct feedback behaviors include ratings or other indications of perceived quality by individuals of specific objects 212 or their attributes. The direct feedback behaviors may reveal the explicit preferences 252 of the user 200. In the adaptive system 100, the adaptive recommendations 250 produced by the adaptive recommendations function 240 (see
A sixth category of usage behaviors 270 is known as physical location behaviors. Physical location behaviors identify physical location and mobility behaviors of the user 200. Location of the user 200 may be inferred from, for example, information associated with a Global Positioning System or any other positionally aware system or device. The physical location of physical objects referenced by objects 212 may be stored in the system 100. Proximity of users 200 to other users 200, or to physical objects referenced by objects 212, may be inferred. The length of time, or duration, at which the user 200 resides in a particular location may be used to infer intensity of interests associated with the particular location, or associated with objects 212 that have a relationship to a physical location.
In addition to the usage behavior categories 246 depicted in Table 1, usage behaviors 270 may be categorized over time and across user behavioral categories 246. Temporal patterns may be associated with each of the usage behavioral categories 246. Temporal patterns associated with each of the categories may be tracked and stored by the adaptive system 100. The temporal patterns may include historical patterns, including how recently an object 212 is accessed. For example, more recent behaviors may be inferred to indicate more intense current interest than less recent behaviors.
Another temporal pattern that may be tracked and contribute to preference inferences made is the duration associated with the access of objects 212, the interaction with the objects 212, or the user's physical proximity to objects 212 that refer to physical objects, or the user's physical proximity to other users 200 of the adaptive system 100. For example, longer durations may generally be inferred to indicate greater interest than short durations. In addition, trends over time of the behavior patterns may be captured to enable more effective inference of interests and relevancy. Since adaptive recommendations 250 may include a combination of structural aspects 210 and content aspects 230, the usage pattern types and preference inferencing may also apply to interactions of the one or more users 200 with the adaptive recommendations 250 themselves.
Adaptive System is Recursive and Iterative
At a subsequent time to the structural aspect update (time n+1), the system use 202 is captured by the adaptive system 100 (block 266). Recall that system use 202, or captured usage information 202, includes any interaction by the one or more users 200 of the adaptive system 100. The use of the system, and hence the captured usage information 202 may be influenced by the updated structural aspects 210 from the previous time period (time n).
As shown in
Multiple invocations of the adaptive recommendations function 240 may be run, automatically or through direct user invocations, synchronously or asynchronously. Each invocation of the adaptive recommendations function 240 performs one or more of the following: 1) providing adaptive recommendations directly to individual users or to or groups of users (communities); 2) updating or modifying the system aspect 210; and, 3) updating or modifying the content aspect 230. The result of this process is multiple, distributed, feedback loops enabling adaptation of the adaptive system 100.
Public Information Framework
The individual usage behaviors 270 can be divided into private behaviors 1120, and non-private behaviors 1130. Private behaviors 1120 are behaviors of a user 200 that are unavailable to other users while non-private behaviors 1130 are behaviors that may be available to other users. As illustrated in
The social information 1140 includes unintentional information or communications, or “cues” 1150, as well as intentional information or communications, or “signals” 1160. Cues 1150 may include by-product information from the intentional communications 1160, whether the cues are derived by the user or users to whom the intentional communications were directed, or by a user or users other than to whom the intentional communications were directed.
An added feature of the adaptive system 100 enables users to specify the level of privacy associated with the derivation of inferred preferences 253 and interests 254. Users 200 may be able to adjust the level of privacy, through a privacy control 1152, associated with the private information 1120 and non-private information 1130 being used by the adaptive system 100 to produce inferred preferences 253 and interests 254. A privacy control 1152a allows the user to enable or disable non-private cues 1150 and signals 1160 from being used to infer preferences and interests. The adjusted level of privacy may be with regard to the tracking of, or the forming of inferences from, the cues 1150 or the signals 1160, to beneficially adapt to the preferences of the user 200. Or, the adjusted level of privacy may be with regard to the tracking of, or the forming of inferences from, the cues 1150 or the signals 1160 that might be used by the adaptive system 100 to provide more effective adaptation to other user's requirements. In other words, the user 200 may choose to wholly or partially “opt out” of the preference inferencing 242 performed by the adaptive system 100, with respect to some or all of the usage behaviors 247 of the user 200.
Memberships in the communities are not necessarily mutually exclusive, as depicted by the overlaps of the sub-community A usage patterns 1006, sub-community B usage patterns 1008, and sub-community C usage patterns 1010 (as well as and the individual usage patterns 1004) in the usage framework 1000. Recall that a community may include a single user 200 or multiple users. Sub-communities may likewise include one or more users 200. Thus, the individual usage patterns 1004 in
The communities identified by the adaptive system 100 may be determined through self-selection, through explicit designation by other users or external administrators (e.g., designation of certain users as “experts”), or through automatic determination by the adaptive system 100. The communities themselves may have relationships between each other, of multiple types and values. In addition, a community may be comprised not of human users, or solely of human users, but instead may include one or more other computer-based systems, which may have reason to interact with the adaptive system 100. Or, such computer-based systems may provide an input into the adaptive system 100, such as by being the output from a search engine. The interacting computer-based system may be another instance of the adaptive system 100.
The usage behaviors 270 included in Table 1 may be categorized by the adaptive system 100 according to the usage framework 1000 of
Multiple usage behavior categories 246 shown in Table 1 may be used by the adaptive system 100 to make reliable inferences based on the preferences, of the user 200 with regard to the content aspect 230 and the structural aspect 210. There are likely to be different preference inferencing 242 results for different users 200. In addition, preference inferencing 242 may be different with regard to optimizing the content aspect 230 for display to the user 200 versus inferred preferences that are used for updating the structural aspect 210 or the content aspect 230, as updates to the structural aspect 210 are likely to be persistent and affect many users.
As an example, simply using the sequences of content accesses as the sole relevant usage behavior on which to base updates to the structure will generally yield unsatisfactory results. This is because the structure itself, through navigational proximity, will create a tendency toward certain navigational access sequence biases. Using just object or content access sequence patterns as the basis for updates to the structural aspect 210 will therefore tend to reinforce the pre-existing structure of the system 100, which may limit the adaptiveness of the adaptive system 100.
By introducing different or additional behavioral characteristics, such as the duration of access of an object 212 or item of content (information 232), on which to base updates to the structural aspect 210 of the system 100 (system structural updates), a more adaptive system is enabled. For example, duration of access will generally be much less correlated with navigational proximity than access sequences will be, and therefore provide a better indicator of true user preferences. Therefore, combining access sequences and access duration will generally provide better inferences and associated system structural updates than using either usage behavior alone. Effectively utilizing additional usage behaviors as described above will generally enable increasingly effective system structural updating. In addition, the adaptive system 100 may employ user affinity groups to enable even more effective system structural updating than are available merely by applying either individual (personal) usage behaviors or entire community usage behaviors.
Furthermore, relying on only one or a limited set of usage behavioral cues 1150 and signals 1160 mitigates against potential “spoofing” or “gaming” of the system 100. “Spoofing” or “gaming” the adaptive system 100 refers to conducting consciously insincere or otherwise intentional usage behaviors 270 so as to influence the adaptive recommendations 250 or changes to the structural aspect 210 by the adaptive system 100. Utilizing broader sets of system usage behavioral cues 1150 and signals 1160 may lessen the effects of spoofing or gaming. One or more algorithms may be employed to detect such contrived usage behaviors, and when detected, such behaviors may be compensated for by the preference and interest inferencing algorithm 242.
As described above, the user 200 of the adaptive system 100 may be a member of one or more communities of interest, or affinity groups, with a potentially varying degree of affinity associated with the respective communities. These affinities may change over time as interests of the user 200 and communities evolve over time. The affinities or relationships among users and communities may be categorized into specific types. An identified user may be considered a member of a special sub-community containing only one member, the member being the identified user. A user can therefore be thought of as just a specific case of the more general notion of user segments, communities, or affinity groups.
Sub-community B 1062 is a community which has many relationships or affinities to other communities. These relationships may be of different types and differing degrees of relevance or affinity. (The relationships between communities depicted in
The relationships 1066 and 1067 may be directionally distinct, and may have an indicator of relationship or affinity associated with each distinct direction of affinity or relationship. For example, the first relationship 1066 has a numerical value 1068, or relationship value, of “0.8.” Several other relationship values are shown in
The relationship value may be scaled as in
The user 1063, which could be considered a user community including a single member, may also have a number of relationships to other communities, where these relationships are of different types, directions and relevance. From the perspective of the user 1063, these relationship types may take many different forms. Some relationships may be automatically formed by the adaptive system 100, for example, based on interests or geographic location or similar traffic/usage patterns. Thus, for example the entire community 1000 may include users in a particular city. Some relationships may be context-relative. For example, a community to which the user 1063 has a relationship could be job-related and another community could be related to another aspect of life, such as related to family, hobby, or health. Thus, sub-community E 1067 may be the employees at a corporation to which the user 1063 has a relationship 1071; sub-community B 1062 may be the members of a sailing club to which the user 1063 has a relationship 1073; sub-community C may be the doctors at a medical facility to which the user 1063 has a relationship 1072. The generation of new communities which include the user 1063 may be based on the inferred interests 254 of the user 1063 or other users within the entire community 1000.
Membership of communities may overlap, as indicated by sub-communities A 1064 and C 1069. The overlap may result when one community is wholly a subset of another community, such as between the entire community 1000 and sub-community B 1062. More generally, a community overlap will occur whenever two or more communities contain at least one user in common. Such community subsets may be formed automatically by the adaptive system 100 based on preference inferencing 242 from usage patterns 248. For example, a subset of a community may be formed based on an inference of increased interest or demand of particular content or expertise of an associated community. The adaptive system 100 is also capable of inferring that a new community is appropriate. The adaptive system 100 will thus create the new community automatically.
For each user, whether residing within, say, sub-community A 1064, or residing outside the community 1000, such as the user 1063, the relationships (such as arrows 1066 or 1067), affinities, or “relationship values” (such as numerical indicator 1068), and directions (of arrows) are unique. Accordingly, some relationships (and specific types of relationships) between communities may be unique to each user. Other relationships, affinities, values, and directions may have more general aspects or references that are shared among many users, or among all users of the adaptive system 100. A distinct and unique mapping of relationships between users, such as is illustrated in
The adaptive system 100 may automatically generate communities, or affinity groups, based on user behaviors 270 and associated preference inferences 242. In addition, communities may be identified by users, such as administrators of the adaptive system 100. Thus, the adaptive system 100 utilizes automatically generated and manually generated communities in generating adaptive recommendations 250.
The communities, affinity groups, or user segments aid the adaptive system 100 in matching interests optimally, developing learning groups, prototyping system designs before adaptation, and many other uses. For example, advanced users of the adaptive system 100 may receive a preview of a new adaptation of a system for testing and fine-tuning, prior to other users receiving this change.
The users 200 or communities may be explicitly represented as objects 212 within the structural aspect 210 or the content aspect 230 of the adaptive system 100. This feature enhances the extensibility (portability) and adaptability of the adaptive system 100.
The user community structure depicted in
Community Preference Inferences
The preferences of a given user community may be inferred from the amount of on-line traffic, or number of accesses or interactions, associated with individual objects 212, or with people or physical objects referenced by the object 212 (this may be termed, “popularity”). The users 200 may have the ability to subscribe to selected structural subsets 280 and assign degrees of personal interest associated with the structural subsets, for the purposes of periodic updates on the structural subsets. Recall that a structural subset is a portion or subset of the structural aspect 210 of a system. The updates may be effected through, for example, e-mail updates.
The relative frequency of structural subsets 280 (e.g., topics) subscribed to by the user community as a whole, or by selected sub-communities, may be used to infer preferences at the community or sub-community level. The users 200 may create their own personalized structural aspect 210 through selection and saving of individual objects 212 or multiple objects and optionally associated relationships or, more generally, structural subsets 280. In such embodiments, the relative frequency of structural subsets being saved in the structural aspect 210 of a particular user by the user community as a whole, or by selected sub-communities, may also be used to infer community and sub-community preferences. These inferred community and sub-community preferences may be derived directly from saved structural subsets 280, but also from direct or indirect affinities the saved structural subsets have with other structural subsets.
Users 200 of the adaptive system 100 may be able to directly rate structural subsets 280 when they are accessed. In such embodiments, the preferences of a community or sub-community may also be inferred through ratings of individual structural subsets. The ratings may apply against both the information 232 referenced by the structural subset 280, as well as meta-information 234 such as an expert review of the information referenced by the system subset. Users 200 may have the ability to suggest structural subsets 280 to one or more other users, and preferences may be inferred from these human-based suggestions. The inferences may be derived from correlating the human-based suggestions with the inferred interests 254 of the receivers if the receivers of the human-based suggestions are users of the adaptive system 100 and have a personal history of objects 212 viewed and/or a personal structural aspect 210 that they may have created.
Expert Preference Inferences
In the adaptive system 100, community subsets, such as subject matter experts, may be designated. Expert opinions on the relationship between objects 212 may be encoded in the structural aspect 210 of the adaptive system 100. Expert views can be directly inferred from the structural aspect. An expert or set of experts may directly rate individual objects and expert preferences may be directly inferred from these ratings.
The history of access of objects 212 or associated meta-information 234 by sub-communities, such as experts, may be used to infer preferences of the associated sub-community. Experts or other user sub-communities may also have the ability to create their own personalized structural aspect 210 through selection and saving or tagging of objects 212. The relative frequency of objects 212 being saved in personal structural aspects 210 (such as a local hard drive) by experts or communities of experts may also be used to infer expert preferences. These inferences may be derived directly from saved or tagged objects 212, but also from affinities the saved objects have with other objects.
A sub-community may be generated by the adaptive system 100 to prototype a new set of adaptive recommendations 250. For example, a sub-community may reflect a newly optimized business process or a frequently traveled path that many novice users of a larger community often follow. In such circumstances, the new set of adaptive recommendations 250 could be useful as a learning tool for new users.
Personal Preference Inferences
Users 200 of the adaptive system 100 may subscribe to selected structural subsets 280 for the purposes of, for example, e-mail updates on these subsets. The objects 212 subscribed to by the user 200 may be used to infer the preferences of the user. Users 200 may create their own personalized structural aspect 210 through selection and saving or tagging of objects 212. The relative frequency of objects 212 being saved in a personal structural aspect 210 by the user 200 may be used to infer the individual preferences of the user. These inferences may be derived directly from saved objects 212, but also from direct or indirect affinities the saved objects have with other objects.
Users can also directly rate objects 212 when accessed. In such embodiments, personal preferences may also be inferred through these ratings of individual objects 212. The ratings may apply against both the information 232 referenced by the object 212, such as an expert review of the information 232 referenced by the object 212. A personal history of paths of the objects 212 viewed may be stored. This personal history can be used to infer preferences of the user 200, as well as tuning adaptive recommendations and suggestions by avoiding recommending or suggesting objects 212 that have already been recently viewed or completed by the user 200.
Adaptive Recommendations and Suggestions
Adaptive recommendations 250 generated by the adaptive 30 recommendations function 240 may combine inferences from community, sub-community (including expert), and personal behaviors and preferences, as discussed above, to present to the one or more users 200, one or more system structural subsets 280. The users 200 may find the structural subsets particularly relevant given the current navigational context of the user within the system, the physical location of the user, and/or responsive to an explicit request of the system by the one or more users. In other words, the adaptive recommendation function 240 determines preference “signals” from the “noise” of system usage behaviors.
The sources of user behavioral information, which typically include the objects 212 referenced by the user 200, may also include the actual information 232 contained therein. In generating adaptive recommendations 250, the adaptive system 100 may thus employ search algorithms that use text matching or more general statistical pattern matching to provide inferences on the inferred themes of the information 232 embedded in, or referenced by, individual objects 212. Furthermore, the structural aspect 210 may itself inform the specific adaptive recommendations 250 generated. For example, existing relationship structures within the structural aspect 210 at the time of the adaptive recommendations 250 may be combined with the user preference inferences based on usage behaviors, along with any inferences based on the content aspect 230 (the information 232).
Delivery of Adaptive Recommendations
The adaptive system 100 begins by determining the relevant usage behavioral patterns 248 to be analyzed (block 283). The adaptive system 100 thus identifies the relevant communities, affinity groups, or user segments of the one or more users 200. Affinities are then inferred among objects 212, structural subsets 280, and among the identified affinity groups (block 284). This data enables the adaptive recommendations function 240 to generate adaptive recommendations 250 of the one or more users 200 for delivery. The adaptive system 100 next determines whether the adaptive recommendations 250 are to be delivered to the recommendations recipients (e.g., users 200 or non-users 260), or are used to update the adaptive system 100 (block 285). Where the recommendations recipients are to receive the adaptive recommendations (the “no” prong of block 285), the adaptive recommendations 250 are generated based on mapping the context of the current system use (or “simulated” use if the current context is external to the actual use of the system) (block 286) to the usage behavior patterns 248 generated by the preference inferencing algorithm 242 (block 286).
Adaptive recommendations are then delivered visually and/or in other communications forms, such as audio, to the recommendations recipients (block 287). The recommendations recipients may be individual users or a group of users, or may be non-users 260 of the adaptive system 100. For Internet-based applications, the adaptive recommendations 250 may be delivered through a web browser directly, or through RSS/Atom feeds and other similar protocols.
The recommended structural subsets 280, along with associated content may constitute most or all of the user interface that is presented to the recommendations recipient, on a periodic or continuous basis. Such embodiments correspond to the continuous, fully adaptive interface described in the framework 2000 of
Where, instead, adaptive system 100 is to receive the adaptive recommendations (the “yes” prong of block 285), the adaptive recommendations 250 are used to update the structural aspect 210 or the content aspect 230. The adaptive recommendations 250 are generated based on mapping the potential structural aspect 210 or content aspect 230 to the affinities generated by the usage behavioral inferences (block 288). The adaptive recommendations 250 are then delivered to enable updating of the structural aspect 210 or the content aspect 230 (block 289).
The adaptive recommendations function 240 may operate completely automatically, performing in the background and updating the structural aspect 210 independent of human intervention. Or, the adaptive recommendations function 240 may be used by users or experts who rely on the adaptive recommendations 250 to provide guidance in maintaining the system structure as a whole, or maintaining specific structural subsets 280 (semi-automatic).
The navigational context for the recommendation 250 may be at any stage of navigation of the structural aspect 210 (e.g., during the viewing of a particular object 212) or may be at a time when the recommendation recipient is not engaged in directly navigating the structural aspect 210. In fact, the recommendation recipient need not have explicitly used the system associated with the recommendation 250.
Some inferences will be weighted as more important than other inferences in generating the recommendation 250. These weightings may vary over time, and across recommendation recipients, whether individual recipients or sub-community recipients. As an example, characteristics of objects 21 which are explicitly stored or tagged by the user 200 in a personal structural aspect 210 would typically be a particularly strong indication of preference as storing or tagging system structural subsets requires explicit action by the user 200. The recommendations optimization algorithms 244 may thus prioritize this type of information to be more influential in driving the adaptive recommendations 250 than, say, general community traffic patterns within the structural aspect 210.
The recommendations optimization algorithm 244 will particularly try to avoid recommending objects 212 that the user is already familiar with to the user. For example, if the user 200 has already stored or tagged the object 212 in a personal structural subset 280, then the object 212 may be a low ranking candidate for recommendation to the user, or, if recommended, may be delivered to the user with a designation acknowledging that the user has already saved or marked the object for future reference. Likewise, if the user 200 has recently already viewed the associated system object (regardless of whether it was saved to his personal system), then the object would typically rank low for inclusion in a set of recommended objects.
The preference inferencing algorithm 242 may be tuned by the individual user. The tuning may occur as adaptive recommendations 250 are provided to the user, by allowing the user to explicitly rate the adaptive recommendations. The user 200 may also set explicit recommendation tuning controls to adjust the adaptive recommendations to her particular preferences. For example, the user 200 may guide the adaptive recommendations function 240 to place more relative weight on inferences of expert preferences versus inferences of the user's own personal preferences. This may particularly be the case if the user was relatively inexperienced in the corresponding domain of knowledge associated with the content aspect 230 of the system, or a structural subset 280 of the system. As the user's experience grows, she may adjust the weighting toward inferences of the user's personal preferences versus inferences of expert preferences.
Adaptive recommendations, which are structural subsets of the adaptive system 100 (sec
In addition to the structural subset 280, the recommendation recipient may be able to access information 232 to help gain an understanding about why the particular structural subset was selected as the recommendation to be presented to the user. The reasoning may be fully presented to the recommendation recipient as desired by the recommendation recipient, or it may be presented through a series of interactive queries and associated answers, where the recommendation recipient desires more detail. The reasoning may be presented through display of the logic of the recommendations optimization algorithm 244. A natural language (e.g., English) interface may be employed to enable the reasoning displayed to the user to be as explanatory and human-like as possible.
The personal preference of the user may affect the nature of the display of the information. For example some users may prefer to see the structural aspect in a visual, graphic format while other users may prefer a more interactive question and answer or textual display.
System users may be explicitly represented as objects in the structural aspect 210 and hence embodied in structural subsets 280. Either embodied as structural subsets, or represented separately from structural subsets 280, the adaptive recommendations 250 of some set of users of the adaptive system 100 may be determined and displayed to recommendation recipients, providing either implicit or explicit permission is granted by the set of users. The recommendations optimization algorithm 244 may match the preferences of other users of the system with the current user. The preference matches may include the characteristics of structural subsets stored or tagged by users, their structural subset subscriptions and other self-profiling information, and their system usage patterns 248. Information about the recommended set of users may be displayed. This information may include names, as well as other relevant information such as affiliated organization and contact information. The information may also include system usage information, such as common system objects subscribed to, etc. As in the case of structural subset adaptive recommendations, the adaptive recommendations of other users may be tuned by an individual user through interactive feedback with the adaptive system 100.
The adaptive recommendations 250 may be in response to explicit requests from the user. For example, a user may be able to explicitly designate one or more objects 212 or structural subsets 280, and prompt the adaptive system 100 for a recommendation based on the selected objects or structural subsets. The recommendations optimization algorithm 250 may put particular emphasis on the selected objects or structural subsets, in addition to applying inferences on preferences from usage behaviors, as well as optionally, content characteristics.
In some embodiments, the adaptive recommendations function 240 may augment the preference inferencing algorithm 242 with considerations related to maximizing the revelation of user preferences, so as to better optimize the adaptive recommendations 250 in the future. In other words, where the value of information associated with reducing uncertainty associated with user preferences is high, the adaptive recommendations function 250 may choose to recommend objects 212 or other recommended structural aspects 210 as an “experiment.” For example, the value of information will typically be highest for relatively new users, or when there appears to be a significant change in usage behavioral pattern 248 associated with the user 200. The adaptive recommendations function 240 may employ design of experiment (DOE) algorithms so as to select the best possible “experimental” adaptive recommendations, and to optimally sequence such experimental adaptive recommendations, and to adjust such experiments as additional usage behaviors 270 are assimilated. The preference inferencing 242 and recommendations optimization 244 algorithms may also preferentially deliver content that is specially sponsored, for example, advertising or public relations-related content.
In summary, the adaptive recommendations 250 may be presented to the users 200, to the non-user 260, or back to the adaptive system 100, for updating either the structural aspect 210 or the content aspect 230. The adaptive recommendations 250 will thus influence subsequent user interactions and behaviors associated with the adaptive system 100, creating a dynamic feedback loop.
Automatic or Semi-Automatic System Structure Maintenance
The adaptive recommendations function 240, optionally in conjunction with system structure maintenance functions, may be used to automatically or semi-automatically update and enhance the structural aspect 210 of the adaptive system 100. The adaptive recommendations function 240 may be employed to determine new relationships 214 among objects 212 in the adaptive system, within structural subsets 280, or structural subsets associated with a specific sub-community. The automatic updating may include potentially assigning a relationship between any two objects to zero (effectively deleting the relationship between the two objects).
In either an autonomous mode of operation, or in conjunction with human expertise, the adaptive recommendations function 240 may be used to integrate new objects 212 into the structural aspect 210, or to delete existing objects 212 from the structural aspect.
The adaptive recommendations function 240 may also be extended to scan and evaluate structural subsets 280 that have special characteristics. For example, the adaptive recommendations function 240 may suggest that certain of the structural subsets that have been evaluated are candidates for special designation. This may include being a candidate for becoming a new specially designated sub-system or structural subset. The adaptive recommendations function 240 will suggest to human users or experts the structural subset 280 that is suggested to become a new sub-system or structural subset, along with existing sub-system or structural subsets that are deemed to be “closest” in relationship to the new suggested structural subset. A human user or expert may then be invited to add the object or objects 212, and may manually create relationships 214 between the new object and existing objects.
As another alternative, the adaptive recommendations function 240, optionally in conjunction with the system structure maintenance functions, may automatically generate the object or objects 212, and may automatically generate the relationships 214 between the newly created object and other objects 212 in the structural aspect 210.
This capability is extended such that the adaptive recommendations function 240, in conjunction with system structure maintenance functions, automatically maintains the structural aspect and identified structural subsets 280. The adaptive recommendations function 240 may not only identify new objects 212, generate associated objects 212, and generate associated relationships 214 among the new objects 212 and existing objects 212, but also identify objects 212 that are candidates for deletion. The adaptive recommendations function 240 may also automatically delete the object 212 and its associated relationships 214.
In this way the adaptive recommendations function 240, optionally in conjunction with a system structure maintenance function, may automatically adapt the structural aspect 210 of the adaptive system 100, whether on a periodic or continuous basis, so as to optimize the user experience.
In some embodiments, each of the automatic steps listed above with regard to updating the structural aspect 210 may be employed interactively by human users and experts as desired.
Hence, the adaptive recommendations function 240, driven in part by usage behaviors, automatically or semi-automatically updates the system structural aspect 210 (see dotted arrow 245 in
Automatic or Semi-Automatic System Content Maintenance
As shown in
The adaptive recommendations function 240 may operate automatically, performing in the background and updating the content aspect 230 independently of human intervention. Or, the adaptive recommendations function 240 may be used by users 200 or special experts who rely on the adaptive recommendations 250 to provide guidance in maintaining the content aspect 230.
As in the case of the structural aspect 210, different communities may also be used to model the maintenance of the content aspect 230. The communities, affinity groups, and user segments are used to adapt the relevancies and to create, alter or delete relationships 214 between the objects 212. The adaptive recommendations 250 may present the objects 212 to the user 200 in a different combination than initially may have been inputted and may treat sections of a larger object such as a document, book or manual as multiple objects that can be recombined in a pattern that is aligned with community usage, by creating or altering relationships between sections.
In addition, as user feedback on system activities and usage behavioral patterns 248 is accumulated, the adaptive system 100 may suggest areas where extra content would be beneficial to users. For example, if the object 212 is frequently rated by users 200 as difficult to understand, or if only expert users in a community are accessing the object, the adaptive system 100 may recognize the need for supplemental content (e.g., in the form of documentation or online tutorials or demonstrations).
Hence, as shown in
Furthermore, the adaptive system 100 may serve as a “user” of another adaptive system. Recall from
Where the adaptive system 100B is less “experienced” (relative to the adaptive system 100A), the adaptive recommendations function 240A may serve as a training mechanism for the new adaptive system 100B. Given a distribution of objects 212 and their relationships 214, metrics and usage behaviors 270 associated with scope, subject and other experiential data such as patterns of other adaptive systems, the adaptive recommendations function 240A may automatically begin assimilation of objects 212 into the less experienced adaptive system 100B, possibly with intervention by human users. Clusters of newly assimilated objects 212 may enable inferences resulting in the suggestion of new structural subsets 280, communities; and their associated relationships would also be, in some embodiments, automatically created and updated. Application of mutual training functionality of the adaptive recommendation engine may also be applied when two or more adaptive systems are directly integrated.
The virtual user (adaptive system 100B) may be integrated with human and non-human users, as depicted in
As with the human user 1206 and the non-human user 1205, virtual users may mutually “use” or interact with one another, as represented by the arrows 201, 203, and 205 leading from the users 200 and the dotted arrow 255 leading from the adaptive recommendations 250A to the users 200B. The mutual interaction between the adaptive systems 100A and 100B enable collective evolution of the structural aspects 210A and 210B and the content aspects 230A and 230B. This principle may be extended to multiple adaptive systems mutually interacting with one another.
The adaptive system 100 is distinguishable from collaborative filtering-based prior art. For example, U.S. Pat. No. 5,790,426, entitled “Automated Collaborative Filtering System” (Robinson) recommends information items based on direct ratings of multiple system users. However, among many other aspects of distinction, the Robinson invention is limited to inferences associated with one type of usage behavior, the direct rating of informational items only, and has no provisions for modifying the system structure or content based on preference inferences.
The structural aspect 210 of the adaptive system 100 may be based on a network structure. The structural aspect 210 thus includes two or more objects, along with associated relationships among the objects. Networks, as used herein, are distinguished from other structures, such as hierarchies, in that networks allow potential relationships between any two objects of a collection of objects. In a network, there are not necessarily well-defined parent objects, and associated children, grandchildren, etc., objects, nor a “root” object associated with the entire system, as there would be by definition in a hierarchy. In other words, networks may include cyclic relationships that are not permitted in strict hierarchies. As used herein, a hierarchy can be thought of as just one particular form of a network, with some additional restrictions on relationships among network objects.
The adaptive system 100 is distinguishable from network-based system structures of the prior art. For example, U.S. Pat. No. 6,285,999, entitled “Method for Node Ranking in a Linked Database” (Page), is a linked node search algorithm that presents a ranking of nodes based on the relative level of linkages among the nodes. However, among many other aspects of distinction, the Page invention is limited to non-fuzzy networks, does not generate persistent structural or content modifications, and does not utilize system usage information as does the adaptive system 100. Another example, U.S. Pat. No. 5,875,446, entitled “System and Method for Hierarchically Grouping and Ranking a Set of Objects in a Query Context Based on One or More Relationships” (Brown, et al), delivers a retrieved set of objects from an object base that has potentially non-directed, weighted relationships, and organizes the retrieved objects in a hierarchical structure. However, among many other aspects of distinction, the Brown, et al, invention does not generate persistent structural or content modifications, does not enable delivery of non-hierarchical structures to users, and does not utilize system usage information, as does the adaptive system 100.
The structural aspect 210 of the adaptive system 100 may also have a fuzzy network structure. Fuzzy networks are distinguished from other types of network structures in that the relationships between objects in fuzzy networks may be by degree. In non-fuzzy networks, the relationships between objects are binary. Thus, between any two objects, relationships either exist or they don't exist.
As used herein, a fuzzy network is defined as a network of information in which each individual item of information may be related to any other individual item of information, and the associated relationship between the two items may be by degree. A fuzzy network can be thought of abstractly as a manifestation of relationships among fuzzy sets (rather than classical sets), hence the designation “fuzzy network.” As used herein, a non-fuzzy network is a subset of a fuzzy network, in which relationships are restricted to binary values (i.e., relationship either exists or does not exist). Pedrycz and Gomide, Introduction to Fuzzy Sets: Analysis and Design, 1998 provide additional background regarding fuzzy sets.
Generalizing further, both classical networks and fuzzy networks may have a-directional (also called non-directed) or directed links between nodes. Four network topologies are listed in Table 2.
links between nodes
type i (classical)
type ii (classical)
type iii (fuzzy)
type iv (fuzzy)
The first two types (i and ii) are classical networks. Fuzzy networks, as used herein, are networks with topologies iii or iv.
For each of the four network topologies listed in Table 2, another possible variation exists: whether the network allows only a single link or multiple links between any two nodes, where the multiple links may correspond to multiple types of links. For example, the fuzzy network types (iii and iv) of Table 2 may permit multiple directionally distinct and multi-valued links between any two nodes in the network. The adaptive system 100 encompasses any of the network topologies listed in Table 2, including those which allow multiple links and multiple link types between nodes.
The relationship among nodes in a fuzzy network may be described most generally by an affinity matrix. For a network with N nodes, n1 . . . ni, for integer i, the affinity matrix will have N rows and columns. Each cell of the matrix contains a number from 0 to 1 that describes the relationship between the associated two nodes, na and nb, 1≦a,b≦i. For classic networks (topology i or ii), each cell of the affinity matrix contains either a 0 or a 1; for fuzzy networks (topology iii or iv), each cell, when normalized, contains a number between 0 and 1, inclusive. If the network allows multiple types of links between any two nodes, then each type of link will have a corresponding affinity matrix associated therewith.
It is instructive to review networks that are familiar and their associated topologies. For example, the World Wide Web, which has been much studied, is generally thought of as a directionally distinct, binary link network (topology ii). In other words, either a web page has a link to another web page or it does not, and the link between the web page and the other web page has a particular direction. (Although there may be multiple links between two web pages, the links are not different in link type, in that they do not have distinctive relationship meanings. The brain, on the other hand, seems to be a fuzzy network, and the links between neurons seem to be generally directionally distinct (Laughlin and Sejnowski, Communication in Neuronal Networks, Science, September 2003). Social networks also seem to be fuzzy networks, and to the links among people may sometimes be modeled as a-directional, but more descriptively may be modeled as directionally distinct.
Mathematically, for a non-fuzzy network, it can be said, without loss of generality, that a relationship translates to either a “0” or a “1”—“0,” for example if there is not a relationship, and “1” if there is a relationship. For fuzzy networks, the relationships between any two nodes, when normalized, may have values along a continuum between 0 and 1 inclusive, where 0 implies no relationship between the nodes, and 1 implies the maximum possible relationship between the nodes. Fundamentally then, fuzzy networks can provide more information about the relationship among network nodes than can non-fuzzy networks.
The structural aspect 210 of the adaptive system 100 of
It can readily be seen that a hierarchy may be described as a directed fuzzy network with the additional restrictions that the relationship values and indicators associated with each relationship must be either “1” or “0” (or the symbolic equivalent). Further, hierarchies do not support cyclic or closed relationship paths.
Although the network structures and variations described herein are represented in the accompanying figures by a network pictorial style, it should be understood that some embodiments may use alternative representations of network structures. These representations may include affinity matrices, as described herein, tabular representations, vector representations, or functional representations. Furthermore, the network operators and algorithms described herein may operate on any of these representations, or on combinations of network representations.
The syndication function 810 may syndicate elements of the usage aspect 220 associated with syndicated structural subsets 280, thus enabling elements of the usage clusters and patterns, along with the corresponding structural subsets, to be combined with other structural subsets and associated usage clusters and patterns.
As explained above, the structural aspect 210 of the adaptive system 100 employs a network structure, and is not restricted to a particular type of network. In some embodiments, the adaptive recombinant system 800 operates on an adaptive system in which the structural aspect 210 is a fuzzy network. The structural subsets 280 generated by the adaptive recombinant system 800 during syndication or recombination are likewise fuzzy networks in these embodiments, and are also called adaptive recombinant fuzzy networks. Recall that a structural subset is a portion or subset of the structural aspect 210 of the adaptive system 100. The structural subset 280 may include a single or multiple objects, and their associated relationships.
Generalized Network Degrees of Separation
The notion of the degree of separation among nodes in non-fuzzy networks is well known. Degrees of separation may be employed as a metric to describe a “neighborhood” within a network. The degree of separation between any two nodes is defined as the shortest path between the two nodes. For networks with directionally distinct relationships between nodes, the shortest path between any two nodes may be specified to adhere to a specific directional orientation.
A node can be thought of as having a zeroth degree of separation with itself. The node has a first degree of separation from other nodes to which it is directly connected. The node has a second degree of separation from the nodes that are directly connected to first degree of separation nodes and are not already more closely separated, and so on.
The notion of degrees of separation of non-fuzzy networks is extended to fuzzy networks in the adaptive system 100. Fractional degrees of separation among nodes may be attributed to fuzzy networks. The degree of separation between the two nodes can be defined as:
for a given affinity level, affinityij, where 0<affinityij≦1, for Node i and Node j, and where 1 is the strongest possible relationship, excluding the identity relationship, and 0 implies no direct relationship. “Scaling factor” is a number between 0 and 1 chosen to normalize the degrees of separation for the fuzzy network consistent with the specific definition and distributions of the affinities between nodes in the fuzzy network.
For example, if an affinity of 1.0 is defined as the identity function, then the scaling factor could be set to 0 so that the degree of separation of an affinity of 1.0, the identity degree of separation, is defined as 0. Alternatively, if an affinity of 0 is defined as no relationship whatsoever, then the degree of separation should logically be greater than 1.0, so the scaling factor may be chosen as a number up to and including 1.0.
The scaling factor may be a function of the specific distribution of the intensity level of affinities in a fuzzy network. These intensities may be linear across the range of 0 and 1, or may be nonlinear. If, for example, the mean intensity is defined at 0.5, then the scaling factor for the fractional degree of separation calculation could be set at 0.5.
In summary, for fuzzy networks, the general case of “distance” relationship between two directly linked nodes is a fractional degree of separation. More generally, the degree of separation between any two nodes in a fuzzy network is defined as the minimum of the degrees of separation (which may be calculated on the basis of a specific directional orientation of relationships among the nodes) among all possible paths between the two nodes, where the degrees of separation between any two nodes along the path may be fractional. Where a network has multiple relationships between nodes, multiple potentially fractional degrees of separation may be calculated between any two nodes in the network.
For convenience, the term fractional degrees of separation may be shortened to the acronym “FREES” (FRactional degrEEs of Separation)—as in, say, “Node X is 2.7 FREES from Node Y.”
The degree of separation within the fuzzy or non-fuzzy network may be calculated and displayed on demand for any two nodes in the network. All nodes within a specified degree of separation of a specified node may be calculated and displayed. Optionally, the associated fractional degrees of separation between the base node and the nodes within the specified fractional degrees of separation may be displayed.
The degrees of separation among nodes in a fuzzy network may be described by a fractional degrees-of-separation (FREES) matrix. For a network with N nodes, n1 . . . nun, the degree-of-separation matrix will have N rows and columns. Each cell of the matrix contains a number that describes the degree of separation between the associated two nodes, in and no. For non-fuzzy networks, each cell will contain an integer value; for fuzzy networks each cell of the FREES matrix may contain non-integer values. For both fuzzy and non-fuzzy networks, the diagonal of the affinity matrix will be 0's—the identity degree of separation. If a fuzzy network is described by multiple affinity matrices, then the multiple affinity matrices correspond on a one-to-one basis with multiple associated FREES matrices.
The degrees of separation for networks with multiple relationship types, whether for fuzzy or non-fuzzy networks, may be calculated as a function across some or all of the relationship types. For example, such a function could be the minimum of degree of separation from Node X to Node Y of all associated relationship types, or the function could be an average, or any other relevant mathematical function.
According to some embodiments, the adaptive recombinant system 800 of
Fuzzy Network Subsets and Adaptive Operators
The adaptive recombinant system 800 of
A selection operator 822, which selects subsets of networks, may designate the selected network subsets based on degrees of separation. For example, subsets of a fuzzy network may be selected from the neighborhood, designated by a FREES metric, around a given node, say Node X. The selection may take the form of selecting all nodes within the designated network neighborhood, or all the nodes and all the associated links as well within the designated network neighborhood, where the network neighborhood is defined as being within a certain degree of separation from Node X. A non-null fuzzy network subset will therefore contain at least one node, and possibly multiple nodes and relationships.
Two or more fuzzy network subsets may then be operated on by network operations such as union, intersection, difference, and complement, as well as any other Boolean set operators. An example is an operation that outputs the intersection (intersection operator 826) of the network subset defined by the first degree or less of separation from Node X and the network subset defined by the second or less degree of separation from Node Y. The operation would result in the set of nodes and relationships common to these two network subsets, with special auxiliary rules optionally applied to resolve duplicative relationships as will be explained below.
The network operations may apply explicitly to fractional degrees of separation. For example, the union operator 824 may be applied to the network subset defined by half a degree of separation (0.5) or less from Node X and the network subset defined as 2.4 degrees of separation or less from Node Y. The union of the two network subsets results in a unique set of nodes and relationships that are contained in both of these network subsets. Special auxiliary rules may optionally be applied to resolve duplicative relationships. Fuzzy network operations may also be chained together, e.g., a union of two network subsets intersected with a third network subset, etc.
The fuzzy network operators 820 may have special capabilities to resolve the situation in which union 824 and intersection 826 operators define common nodes, but with differing relationships or values of the relationships among the common nodes. The fuzzy network intersection operator 826, Fuzzy_Network_Intersection, may be defined as follows:
Z=Fuzzy_Network_Intersection(X, Y, W)
where X, Y, and Z are network subsets and W is the resolution function 834. The resolution function 834 designates how duplicative relationships among nodes common to fuzzy network subsets X and Y are resolved.
Specifically, the fuzzy network intersection operator 826 first determines the common nodes of network subsets X and Y, to form a set of nodes, network subset Z. The fuzzy network intersection operator 826 then determines the relationships and associated relationship value and indicators uniquely deriving from X among the nodes in Z (that is, relationships that do not also exist in Y), and adds them into Z (attaching them to the associated nodes in Z). The operator then determines the relationships and relationship indicators and associated values uniquely deriving from Y (that is, relationships that do not also exist in X) and applies them to Z (attaching them to the associated nodes in Z).
For relationships that are common to X and Y, the resolution function 834, is applied. The resolution function 834 may be any mathematical function or algorithm that takes the relationship values of X and Y as arguments, and determines a new relationship value and associated relationship indicator.
The resolution function 834, Resolution_Function may be a linear combination of the corresponding relationship value of X and the corresponding relationship value of Y, scaled accordingly. For example:
Resolution_Function (XRV, YRV)=(c1*XRV+c2*YRV)/(c1+c2)
where XRV and YRV are relationship values of X and Y, respectively, and c1 and c2 are coefficients. If c1=1, and c2=0, then XRV completely overrides YRV. If c1=0 and c2=1, then YRV completely overrides XRV. If c1=1 and c2=1, then the derived relationship is a simple average of XRV and YRV. Other values of c1 and c2 may be selected to create weighted averages of XRV and YRV. Nonlinear combinations of the associated relationships values, scaled appropriately, may also be employed.
The Fuzzy_Network_Union operator 824 may be derived from the Fuzzy_Network_Intersection operator 826, as follows:
Z=Fuzzy_Network_Union(X, Y, W)
where X, Y and Z are network subsets and W is the resolution function 834. Accordingly,
Z=Fuzzy_Network_Intersection(X, Y, W)+(X−Y)+(Y−X)
That is, fuzzy network unions of two network subsets may be defined as the sum of the differences of the two network subsets (the nodes and relationships that are uniquely in X and Y, respectively) and the fuzzy network intersection of the two network subsets. The resulting network subset of the difference operator contains any unique relationships between nodes uniquely in an originating network subset and the fuzzy network intersection of the two subsets. These relationships are then added to the fuzzy network intersection along with all the unique nodes of each originating network subset, and all the relationships among the unique nodes, to complete the resulting fuzzy network subset.
It should be noted that, unlike the corresponding classic set operators, the fuzzy network intersection 826 and union 824 operators are not necessarily mathematically commutative—that is, the order of the operands may matter. The operators will be commutative if the resolution function or algorithm is commutative.
For the adaptive recombinant system 800, the resolution function 834 that applies to operations that combine multiple networks may incorporate usage behavioral inferences related to one or all of the networks. The resolution function 834 may be instantiated directly by the adaptive recommendations function 240 (
For example, where one of the predecessor networks was used by larger numbers of individuals, or by individuals that members of communities or affinity groups that are inferred to be best informed on the subject of the associated content, then the resolution function 834 may choose to preferentially weight the relationships of that predecessor network higher versus the other predecessor networks. The resolution function 834 may use any or all of the usage behaviors 270, along with associated user segmentations and affinities obtained during usage behavior pre-processing 204 (see
Fuzzy Network Metrics
Special metrics may be used to measure the characteristics of fuzzy networks and fuzzy network subsets. For example, these metrics may provide measures associated with the relationship of a network node or object to other parts of the network, and relative to other network nodes or objects. A metric may be provided that indicates the degree to which nodes are connected to the rest of the network. This metric may be calculated as the sum of the affinities of first degree or less separated directionally distinct relationships or links. The metric may be called a first degree connectedness parameter for the specific node.
The first degree connectedness metric may be generalized for zeroth to Nth degrees of connectedness as follows. The zeroth degree of connectedness is, by definition, zero. The Nth degree of connectedness of Node X is the sum of the affinities among all nodes within N degrees of separation of Node X. For fuzzy networks, N may not necessarily be an integer value. The connectedness parameters may be indexed to provide a convenient relative metric among all other nodes in the network.
As an example, in the fuzzy network 630 of
In networks in which there are multiple types of relationships among nodes, there may be multiple connectedness measures for any specific Node X to the subset of the fuzzy network specified by a degree of separation, N, from X.
In summary, connectedness for a specific Node X may have variations associated with relationship type, the specified directions of the relationships selected for computation, and the degree of separation from the Node X. The general connectedness metric function may be defined as follows:
Connectedness(Node X, T, D, S)
where T is the relationship indicator type, D is the relationship direction, and S is the degree of separation. The Connectedness metric may be normalized to provide a convenient relative measure by indexing the metric across all nodes in a network.
A metric of the popularity of the network nodes or objects, or popularity metric, may also be provided. The fuzzy or non-fuzzy network may be implemented on a computer system, or on a network of computer systems such as the Internet or on an Intranet. The system usage behavioral patterns of users of the fuzzy network may be recorded. The number of accesses of particular nodes or objects of a fuzzy to non-fuzzy network may be recorded. The accesses may be defined as the actual display of the node or object to the user or the accesses may be defined as the display of information associated with the node or object to user, such as access to an associated editorial review. In some of these embodiments, the popularity metric may be based on the number of user accesses of the associated node or object, or associated—information. The popularity metric may be calculated for prescribed time periods. Popularity may be recorded for various user segments, in addition to, or instead of, the usage associated with the entire user community. The usage traffic may be stored so that popularity trends over time may be accessed. In the most general case, popularity for a specific Node X will have variations by user segments and time periods. A general popularity function may therefore be represented as follows:
Popularity(Node X, user segment, time period)
The Popularity metric may be normalized to provide a convenient relative measure by indexing the metric across all nodes in a network.
Metrics may be generated that go beyond the connectedness metrics, to provide information on additional characteristics associated with a node or object within the network relative to other nodes or objects in the network. A metric that combines aspects of connectedness and popularity measures, an influence metric, may be generated. The influence metric may provide a sense of the degree of importance or “influence” a particular node or object has within the fuzzy network.
The influence metric for Node X is calculated by adding the popularity of Node X to a term that is the sum of the popularities of the nodes or objects separated by one degree of separation or less from Node X, weighted by the associated affinities between Node X and each associated related node. The term associated with the weighted average of the popularities of the first degree of separation nodes of Node X is scaled by a coefficient. This coefficient may be defined as the inverse of the first degree connectedness metric of Node X.
For fuzzy networks with directionally distinct relationships and affinities, the influence metric may be calculated based only on the first degree affinities or less for relationships that are oriented in a particular direction. For example, influence may be calculated based on all relationships directed to Node X (as opposed to those directed away from Node X).
A generalized influence metric may also be provided, where the Nth degree of influence of node or object X is defined as the popularity of Node X added to a term that is the weighted average of the popularities of all nodes within N degrees of separation from Node X (where N may be a non-integer, implying a fractional degree of separation). The weights for each node may be a function of the affinities of the shortest path between Node X and the associated node. The generalized influence metric may be a multiplicative function, that is, the affinities along the path from Node X to each node within N degrees separation are multiplied together and then multiplied by the popularity of the associated node. Or, the metric may be a summation function, or any other mathematical function that combines the affinities along the associated network path. The generalized influence metric may be specified as a recursive function, satisfying the following difference equations and “initial condition”:
Nth Degree of Influence(Node X)=(N−1)th Degree of Influence(Node X)+Influence of Nodes of N Degrees of Separation from Node X. (1)
Zeroth Degree of Influence(Node X)=Popularity(Node X) (2)
Where there are directionally distinct affinities, the affinities that are multiplied, summed, or otherwise mathematically operated on, between Node X and all other nodes within a directionally distinct degree of separation (where the degree of separation may be fractional), may be of relationships with a selected directional orientation. The relationship direction term (D, in the connectedness metric function, above, may be scaled by the Nth degree of connectedness (of a given directional orientation) of Node X.
The zeroth degree of influence may be defined as just the popularity of Node X. The Nth degree of influence is indexed to enable convenient comparison of influence among nodes or objects in the network. Where there are multiple types of relationships between any two nodes in the network, influence may be calculated for each type of relationship. An influence metric may also be generated that averages (or applies any other mathematical function that combines values) across multiple influence metrics associated with two or more relationship types.
The second degree of influence of Node X is calculated as the first degree of influence of Node X (already calculated) plus the influence contributed by each node that is two degrees of separation from Node X, and may likewise be calculated, as follows:
Table 3 lists the first degree affinities, second degree affinities, popularity, calculated influence, and cumulative influence, relative to Node X, for the fuzzy network 650 of
Affinity, popularity, & influence data for fuzzy network 650.
1st ° affinities
2nd ° affinities
In summary, the influence metric for Node X may have variations associated with a specific relationship indicator type, a specific direction of relationships for the relationship indicator type, a degree of separation from Node X, and a scaling coefficient that tunes the desired degradation of weighting for nodes and relationships increasingly distant from Node X. The metric function may therefore be represented as follows:
Influence(Node X, relationship indicator type or types, relationship direction, degree of separation, affinity path function, scaling coefficient). The influence metric may be normalized to provide a convenient relative measure by indexing the metric across all nodes in a network. Metrics associated with nodes of fuzzy networks, such as popularity, connectedness, and influence, may be displayed in textual or graphical forms to users of the fuzzy network-based system. The adaptive recombinant system 800 of
Fuzzy Network Syndication and Combination
The adaptive recombinant system 800 of
The adaptive system 100 of
Similarly, the adaptive recombinant system 800 of
The structural aspect 210 of adaptive system 100 may be comprised of multiple structures, comprising network-based structures, non-network-based structures, or combinations of network-based structures and non-network-based structures. In
The adaptive recombinant system 800 may determine objects, such as the objects 522 and 532 of
The combination of the fuzzy network 520 and the fuzzy network 530 yields fuzzy network 540. In the fuzzy network 540, relationships that were unique in networks 520 and 530 are maintained. Where relationships or relationship indicators are common in fuzzy networks 520 and 530, the resolution function 834 (
For example, object 522 and object 532 are part of both fuzzy network 520 and fuzzy network 530. A relationship 521 is depicted between object 522 and object 532 in the fuzzy network 520, while a relationship 531 is depicted between object 522 and object 532 in the fuzzy network 530. Where relationships 521 and 530 are of the same type, the resulting relationship indicators 541 in the newly created fuzzy network 540 is an average of relationship indicators 521 and 531. That is, for determining the relationship between objects 522 and 532 in the fuzzy network 540, the resolution function 834 is a simple average function. In general, the resolution function 834 may be any mathematical function or algorithm that takes as input two numbers between 0 and 1 inclusive, and outputs a number between 0 and 1 inclusive.
The resolution function 834 may be derived from algorithms that apply appropriate usage behavior inferences. As a simple example, if the relationship value and associated indicator of one network has been derived from the usage behaviors of highly informed or expert users, then this may have more weighting than the relationship value and associated indicator of a second network for which the corresponding relationship value was based on inferences associated with the usage behaviors of a relatively sparse set of relatively uniformed users.
New relationships and associated relationship indicators that do not exist in originating fuzzy networks may also be generated by the adaptive recombinant system 800 upon fuzzy network creation. The adaptive recommendations function 240 may be invoked directly to effect such relationship modifications, or it may be invoked in conjunction with fuzzy network maintenance functions.
For example, in
The determination of a new relationship may be based on fuzzy network structural, usage, or content characteristics, and associated inferencing algorithms. For example, in predecessor network 530, the traffic patterns, combined with the organization of user referenced subsets of 530, as one example, may support adding the relationship 542 in the new network 540 that did not exist in the predecessor networks. The same procedure may be used to delete existing relationships (which may be alternatively viewed as just equivalent to setting a relationship indicator to “0”), as desired. The algorithms for modifying relationships and relationship indicators, including adding and deleting relationships, may incorporate global considerations with regard to optimizing the overall topology of the fuzzy network by creating effective balance of relationships among objects to maximize overall usability of the network.
The adaptive recombinant system 800 of
The adaptive recombinant system 800 may efficiently support multiple adaptive systems 100, without reproducing the components used to support syndication and recombination for each adaptive system.
In addition to the resolution function 834, the adaptive recombinant system 800 may use the object evaluation function 830, to evaluate the “fitness” of the recombined fuzzy networks. The object evaluation function 830 may be completely automated, or it may incorporate explicit human judgment. The networks that are evaluated to be most fit are then recombined among themselves, to create a new generation of fuzzy networks.
The adaptive recombinant system 800 may also create random structural changes to enhance the diversity of the fuzzy networks in the next generation. Or, the adaptive recombinant system 800 may use explicit non-random-based rules to enhance the diversity of the fuzzy networks in the next generation. Preferably, the inheritance characteristics from generation to generation of adaptive recombinant fuzzy networks may be that of acquired traits (Lamarckian). Or, the inheritance characteristics from generation to generation of adaptive recombinant fuzzy networks may be that of non-acquired, or random mutational, traits (Darwinian). For the Lamarckian embodiments, the acquired traits include any structural adaptations that have occurred through system usage, syndications, and combinations.
Through application of these multi-generational approaches, fuzzy networks are able to evolve against the selection criteria that are provided. The fitness selection criteria may be determined through inferences associated with fuzzy network usage behaviors, and may itself co-evolve with the generations of adaptive fuzzy networks.
Fuzzy Content Network
In some embodiments, the structural aspect 210 of the adaptive system 100 and of the adaptive recombinant system 800, as well as the respective structural subsets 280 generated by the adaptive recommendations function 240, are networks of a particular form, a fuzzy content network. A fuzzy content network 700 is depicted in
The fuzzy content network 700, including content sub-networks 700a, 700b, and 700c. The content network 700 includes “content,” “data,” or “information,” packaged in modules known as objects 710.
The content network 700 employs features commonly associated with “object-oriented” software to manage the objects 710. That is, the content network 700 discretizes information as “objects.” In contrast to typical procedural computer programming structures, objects are defined at a higher level of abstraction. This level of abstraction allows for powerful, yet simple, software architectures.
One benefit to organizing information as objects is known as encapsulation. An object is encapsulated when only essential elements of interaction with other objects are revealed. Details about how the object works internally may be hidden. In
Another benefit to organizing information as objects is known as inheritance. The encapsulation of
In the content network 700, the objects 710 may be either topic objects 710t or content objects 710c, as depicted in
Content objects 710c, as shown in
The referenced information 714 may include files, text, documents, articles, images, audio, video, multi-media, software applications and electronic or magnetic media or signals. Where the content object 714c supplies a pointer to information, the pointer may be a memory address. Where the content network 700 encapsulates information on the Internet, the pointer may be a Uniform Resource Locator (URL).
The meta-information 712 supplies a summary or abstract of the object 710. So, for example, the meta-information 712t for the topic object 710t may include a high-level description of the topic being managed. Examples of meta-information 712t include a title, a sub-title, one or more descriptions of the topic provided at different levels of detail, the publisher of the topic meta-information, the date the topic object 710t was created, and subjective attributes such as the quality, and attributes based on user feedback associated with the referenced information. Meta-information may also include a pointer to referenced information, such as a uniform resource locator (URL), in one embodiment.
The meta-information 712c for the content object 710c may include relevant keywords associated with the information 714, a summary of the information 714, and so on. The meta-information 712c may supply a “first look” at the objects 710c. The meta-information 712c may include a title, a sub-title, a description of the information 714, the author of the information 714, the publisher of the information 714, the publisher of the meta-information 712c, and the date the content object 710c was created, as examples. As with the topic object 710t, meta-information for the content object 710c may also include a pointer.
The relationship indicator 718 is a numerical indicator of the relationship between objects 710. Thus, for example, the relationship indicator 718 may be normalized to between 0 and 1, inclusive, where 0 indicates no relationship, and 1 indicates a subset relationship. Or, the relationship indicators 718 may be expressed using subjective descriptors that depict the “quality” of the relationship. For example, subjective descriptors “high,” “medium,” and “low” may indicate a relationship between two objects 710.
The relationship 716 between objects 710 may be bidirectional, as indicated by the double-pointing arrows. Each double-pointing arrow includes two relationship indicators 718, one for each “direction” of the relationships between the objects 710.
The content networks 700A, 700B, 700C may be related to one another using relationships of multiple types and associated relationship indicators 718. For example, in
Individual content and topic objects 710 within a selected content sub-network 700a may be related to individual content and topic objects 710 in another content sub-network 700b. Further, multiple sets of relationships of multiple types and associated relationship indicators 718 may be defined between two objects 710
For example, a first set of relationships 716 and associated relationship indicators 718 may be used for a first purpose or be available to a first set of users while a second set of relationships 716 and associated relationship indicators 718 may be used for a second purpose or available to a second set of users. For example, in
The relationships among objects 710 in the content network 700, as well as the relationships between content networks 700a and 700b, may be modeled after fuzzy set theory. Each object 710, for example, may be considered a fuzzy set with respect to all other objects 710, which are also considered fuzzy sets. The relationships among objects 710 are the degrees to which each object 710 belongs to the fuzzy set represented by any other object 710. Although not essential, every object 710 in the content network 700 may conceivably have a relationship with every other object 710.
The topic objects 710t encompass, and are labels for, very broad fuzzy sets of the content network 700. The topic objects 710t thus may be labels for the fuzzy set, and the fuzzy set may include relationships to other topic objects 710t as well as related content objects 710c. Content objects 710c, in contrast, typically refer to a narrower domain of information in the content network 700.
The adaptive system 100 of
Similarly, the adaptive recombinant system 800 of
Extended Fuzzy Structures in Fuzzy Networks
The fuzzy network model may be extended to the organizational structure of the meta-information and other affiliated information associated with each network node or object. In a fractional degree of separation system structure, depicted in
Meta-information 754 associated with information or interactive applications 752 may include, but is not limited to, descriptive information about the object such as title, publishing organization, date published, physical location of a physical object, an associated photo or picture, summary or abstracts, a plurality of reviews, etc. Meta-information 754 may also include dynamic information such as expert and community ratings of the information, feedback from users, and more generally, any relevant set of, or history of, usage behaviors described in Table 1. The meta-information 754 may also include information about relationships to other nodes in the network. For example, the meta-information 754 may include the relationships with other nodes in the networks, including an identification code for each related node, the types of relationships, the direction of the relationships, and the degree of relatedness of each relationship.
The meta-information 754 may be defined within tiers of fractional degree of separation between zero and one. For example, the most tightly bound meta-information might be in a tier at degree of separation of 0.1 and less tightly bound meta-information might be in a tier at degree of separation of 0.8.
Where the degrees of separation calculated between any two nodes in the fuzzy network are between 0 and 1, the meta-information tiers would more appropriately be designated with negative (possibly fractional) degrees of separation. For example, the most tightly bound meta-information 752 may be in a tier at degree of separation of −5 and less tightly bound meta-information may be in a tier at degree of separation of −1.
The meta-information tiers may distinguish between static meta-information such as the original author of the associated information, and dynamic information such as the total number of accesses of the associated information through a computer system.
The fractional degree of separations of less than one may correspond to compound objects 756. For example, a picture object plus a text biography object may constitute a person object. For typical fuzzy content network operations the compound object would generally behave as if it was one object.
The fractional degree of separations of less than one may correspond to a list of objects with which the present object has a specific sequential relation 758. For example, this may include workflow sequences in processes. These sequential relationships imply a tighter “binding” between objects than the relationships associated with other objects in the fuzzy network 770, hence a smaller fractional degree of separation is employed for sequential relationships.
All meta-information may explicitly be content objects that relate to associated information by a fractional degree of separation of less than one, and may relate to other content objects in the network by a fractional degree of separation that may be greater than or equal to one. This can be described by a degree-of-separation matrix. Every object is arrayed in sequence along both the matrix columns and the matrix rows. Each cell of the matrix corresponds to the degree of separation between the two associated objects. The cells in the main diagonal of the degree of separation matrix are all zeroes, indicating the degree of separation between an object and itself is zero. All other cells will contain a non-zero number, indicating the degree of separation between the associated objects, or a designator indicating that the degree of separation is essentially infinite in the case when there is no linked path at all between the associated objects.
Personalized Fuzzy Content Network Subsets
Recall that users 200 of the adaptive system 100 of
Users of the Epiture software system may select content objects and tag them for storage in their personal fuzzy network. Optionally, related meta-information and links to other objects in the original fuzzy network may be stored with the content object. Users may also store entire topics in their “My World” personal fuzzy network. Furthermore, users may use fuzzy network operators to create synthetic topics. For example, a user might apply an intersection operator to Topic A and Topic B, to yield Topic C. Topic C could then be stored in the personal fuzzy network. Union, difference and other fuzzy network operators may also be used in creating new fuzzy network subsets to be stored in a private fuzzy content network.
Users of the Epiture software system may directly edit their personal fuzzy networks, including the names or labels associated with content objects and topic objects, as well as other meta-information associated with content and topic objects. The screenshot 770 of
Users may selectively share their personal fuzzy networks by allowing other users to have access to their personal networks. Convenient security options may be provided to facilitate this feature.
Usage Behavior Information
Users of the Epiture software system may have the ability to review personal, sub-community or community usage behaviors over time. This may include trends related to popularity, connectedness, influence or any other relevant usage metric.
Navigational histories, such as access paths, may be available for review, with capabilities for making queries against the histories though application of selection criteria.
Users may also have access to system usage information that may be captured and organized to retain temporal information associated with usage behaviors, including the duration of behaviors and the timing of the behaviors, where the behaviors may include those associated with reading or writing of written or graphical material, oral communications, including listening and talking, or duration of physical location of a system user, potentially segmented by user communities or affinity groups may be available for review by users.
The above usage behaviors may be available to users in raw form, or in summarized form, potentially after application of statistical or other mathematical functions are applied to facilitate interpretation. This information may be presented in a graphical format.
Adaptive Recommendations in Fuzzy Content Networks
Adaptive recommendations or suggestions may enable users to more effectively navigate through the fuzzy content network. As with other network embodiments described herein, the adaptive recommendations generated from a fuzzy content network may be in the context of a currently accessed content object or historical path of accessed content objects during a specific user session, or the adaptive recommendations may be without context of a currently accessed content object or current session path.
In the most generalized approach, adaptive recommendations in a fuzzy content network combine inferences from user community behaviors and preferences, inferences of sub-community or expert behaviors and preferences, and inferences of personal user behaviors and preferences. Usage behaviors that may be used to make preference inferences include, but are not limited to, those that are described in Table 1. These usage-based inferences may be augmented by automated inferences about the content within individual and sets of content objects using statistical pattern matching of words or phrases within the content. Such statistical pattern matching may include, but not limited to, Bayesian analysis, neural network-based methods, k-nearest neighbor, support vector machine-based techniques, or other statistical analytical techniques.
Community Preference Inferences
Where the structural aspect 210 of the adaptive system 100 or the adaptive recombinant system 800 is a fuzzy content network, user community preferences may be inferred from the popularity of individual content objects and the influence of topic or content objects, as popularity and influence were defined above. The duration of access or interaction with topic or content objects by the user community may be used to infer preferences of the community.
Users may subscribe to selected topics, for the purposes of e-mail updates on these topics. The relative frequency of topics subscribed to by the user community as a whole, or by selected sub-communities, may be used to infer community or sub-community preferences. Users may also create their own personalized fuzzy content networks through selection and saving of content objects and/or topics objects. The relative frequency of content objects and/or topic objects being saved in personal fuzzy content networks by the user community as a whole, or by selected sub-communities, may be used to also infer community and sub-community preferences. These inferences may be derived directly from saved content objects and/or topics, but also from affinities the saved content and/or topic objects have with other content objects or topic objects. Users can directly rate content objects when they are accessed, and in such embodiments, community and sub-community preferences may also be inferred through these ratings of individual content objects.
The ratings may apply against both the information referenced by the content object, as well as meta-information such as an expert review of the information referenced by the content object. Users may have the ability to suggest content objects to other individuals and preferences may be inferred from these human-based suggestions. The inferences may be derived from correlating these human-based suggestions with inferred interests of the receivers if the receivers of the human-based suggestions are users of the fuzzy content object system and have a personal history of content objects viewed and/or a personal fuzzy content network that they may have created.
The physical location and duration of remaining in a location of the community of users, as determined by, for example, a global positioning system or any other positionally aware system or device associated with users or sets of users, may be used to infer preferences of the overall user community.
Sub-Community and Expert Preference Inferences
Community subsets, such as experts, may also be designated. Expert opinions on the relationship between content objects may be encoded as affinities between content objects. Expert views may be directly inferred from these affinities. An expert or set of experts may directly rate individual content items and expert preferences may be directly inferred from these ratings.
The history of access of objects or associated meta-information by sub-communities, such as experts, may be used to infer preferences of the associated sub-community. The duration of access or interaction with objects by sub-communities may be used to infer preferences of the associated sub-community.
Experts or other user sub-communities may have the ability to create their own personalized fuzzy content networks through selection and saving of content objects. The relative frequency of content objects saved in personal fuzzy content networks by experts or communities of experts may be used to also infer expert preferences. These inferences may be derived directly from saved content objects, but also from affinities the saved content objects have with other content objects or topic objects.
The physical location and duration of remaining in a location of sub-community users, as determined by, for example, a global positioning system or any other positionally aware system or device associated with users or sets of users, may be used to infer preferences of the user sub-community.
Personal Preference Inferences
Users may subscribe to selected topics, for the purposes of, for example, e-mail updates on these topics. The topic objects subscribed to by the user may be used to infer personal preferences. Users may also create their own personalized fuzzy content networks through selection and saving of content objects. The relative frequency of content objects saved in personal fuzzy content networks by the user may be used to infer the individual's personal preferences. These inferences may be derived directly from saved content objects, but also from affinities the saved content objects have with other content objects or topic objects. Users may directly rate content objects when they are accessed, and in such embodiments, personal preferences may also be inferred through these ratings of individual content objects.
The ratings may apply against both the information referenced by the content object, as well as any of the associated meta-information, such as an expert review of the information referenced by the content object. A personal history of paths of content objects viewed may be stored. This personal history may be used to infer user preferences, as well as tuning adaptive recommendations and suggestions by avoiding recommending or suggesting content objects that have already been recently viewed by the individual. The duration of access or interaction with topic or content objects by the user may be used to infer preferences of the user.
The physical location and duration of remaining in a location of the user as determined by, for example, a global positioning system or any other positionally aware system or device associated with the user, may be used to infer preferences of the user.
Adaptive Recommendations and Suggestions
Adaptive recommendations in fuzzy content networks combine inferences from user community behaviors and preferences, inferences of sub-community or expert behaviors and preferences, and inferences of personal user behaviors and preferences as discussed above, to present to a fuzzy network user or set of users one or more fuzzy network subsets (one or more objects and associated relationships) that users may find particularly interesting given the user's current navigational context. These sources of information, all of which are external to the referenced information within specific content objects, may be augmented by search algorithms that use text matching or statistical pattern matching or learning algorithms to provide information on the likely themes of the information embedded or pointed to by individual content objects.
The navigational context for a recommendation may be at any stage of navigation of a fuzzy network (e.g., during viewing a particular content object) or may be at a time when the recommendation recipient is not engaged in directly navigating the fuzzy network. In fact, the recommendation recipient need never have explicitly used the fuzzy network associated with the recommendation. As an example,
Some inferences will be weighted as more important than other inferences in generating a recommendation, and theses weightings may vary over time, and across recommendation recipients, whether individual recipients or sub-community recipients. For example, characteristics of content and topics explicitly stored by a user in a personal fuzzy network would typically be a particularly strong indication of preference as storing network subsets requires explicit action by a user. In most recommendation algorithms, this information will therefore be more influential in driving adaptive recommendations than, say, general community traffic patterns in the fuzzy network.
The recommendation algorithm may particularly try to avoid recommending to a user content that the user is already familiar with. For example, if the user has already stored a content object in a personal fuzzy network, then the content object might be a very low ranking candidate for recommending to the user. Likewise, if the user has recently already viewed the associated content object (regardless of whether it was saved to his personal fuzzy network), then the content object would typically rank low for inclusion in a set of recommended content objects. This may be further tuned through inferences with regard to the duration that an associated content object was viewed (for example, it may be inferred that a lengthy viewing of a content object is indicative of increased levels of familiarity.
The algorithms for integrating the inferences may be tuned or adjusted by the individual user. The tuning may occur as adaptive recommendations are provided to the user, by allowing the user to explicitly rate the adaptive recommendations. The user may also set explicit recommendation tuning controls to tune the adaptive recommendations to her particular preferences. For example, a user might guide the recommendation function to place more relative weight on inferences of expert or other user communities' preferences versus inferences of the user's own personal preferences. This might be particularly true if the user was relatively inexperienced in the particular domain of knowledge. As the user's experience grew, he might adjust the weighting toward inferences of the user's personal preferences versus inferences of expert preferences.
Fuzzy network usage metrics described above such as popularity, connectedness, and influence may be employed by the recommendation algorithm as convenient summaries of community, sub-community and individual user behavior with regard to the fuzzy network. These metrics may be used individually or collectively by the recommendation algorithm in determining the recommended network subset or subsets to present to the recommendation recipient.
Adaptive recommendations which are fuzzy network subsets may be displayed in variety of ways to the user. They may be displayed as a list of content objects (where the list may be null or a single content object), they may include content topic objects, and they may display a varying degree of meta-information associated with the content objects and/or topic objects. Adaptive recommendations may be delivered through a web browser interface, through e-mail, through instant messaging, through XML-based feeds, RSS, or any other approach in which the user visually or acoustically interprets the adaptive recommendations. The recommended fuzzy network subset may be displayed graphically. The graphical display may provide enhanced information that may include depicting linkages among objects, including the degree of relationship, among the objects of the recommended fuzzy network subset, and may optionally indicate through such means of size of displayed object or color of displayed object, designate usage characteristics such as popularity of influence associated with content objects and topic objects in the recommended network subset. Adaptive recommendations may be delivered for interpretation of users by other than visual senses; for example, the recommendation may be delivered acoustically, typically through oral messaging.
The recommended structural subsets 280, combinations of topic objects, content objects, and associated relationships, may constitute most or even all of the user interface, which may be presented to a system user on a periodic or continuous basis. Such embodiments correspond to embodiment variations of 2130, 2140, 2150 and 2160 of the framework 2000 in
In addition to the recommended fuzzy network subset, the recommendation recipient may be able to access information to help gain an understanding from the system why the particular fuzzy network subset was selected as the recommendation to be presented to the user. The reasoning may be fully presented to the recommendation recipient as desired by the recommendation recipient, or it may be presented through a series of interactive queries and associated answers, as a recommendation recipient desires more detail. The reasoning may be presented through display of the logic of the recommendation algorithm. A natural language (such as English) interface may be employed to enable the reasoning displayed to the user to be as explanatory and human-like as possible.
In addition to adaptive recommendations of fuzzy network subsets, adaptive recommendations of some set of users of the fuzzy network may be determined and displayed to recommendation recipients, typically assuming either implicit or explicit permission is granted by such users that might be recommended to other users. The recommendation algorithm may match preferences of other users of the fuzzy network with the current user. The preference matches may include the characteristics of fuzzy network subsets stored by users or other fuzzy network referencing, their topic subscriptions and self-profiling, their collaborative patterns, their direct feedback patterns, their physical location patterns, their fuzzy network navigational and access patterns, and related temporal cues associated with these usage patterns. Information about the recommended set of users may be displayed to a user. This information may include names, as well as other relevant information such as affiliated organizations and contact information. It may also include fuzzy network usage behavioral information, such as, for example, common topics subscribed to, common physical locations, etc. As in the case of fuzzy network subset adaptive recommendations, the adaptive recommendations of other users may be tuned by an individual user through interactive feedback with the system.
The vertical dimension 2002 includes four categories across a range, the first category being least adaptive and the fourth category being the most adaptive. The categories are: non-adaptive (does not dynamically customize); displays adaptive recommendations 250 (where “displays” includes not only visual delivery of adaptive recommendations, but delivery in other modes, such as audio); provides adaptive recommendations 250 that update structure or content (where the structure and/or content of the system are dynamically updated); and provides a continuous, fully adaptive interface. The adaptive system 100 and the adaptive recombinant system 800 are capable of all degrees of adaptiveness depicted in the framework 2000, including providing a continuous, fully adaptive interface.
The horizontal dimension 2004 of the framework 2000 represents the degree of extensibility of the identified system. The “degree of extensibility” or “degree of portability” denotes the ability to “syndicate” the system 100 or subsets of the system 100, as well as the ability to create combinations of systems. Syndication, as used herein, describes ability to share systems or portions of systems, which may include actual transfer of the system structural and content aspects across computer and communications network hardware, or may describe the virtual transfer of a system on a particular set of computer hardware. Recall that a structural subset 280 is a portion of the structural aspect 210 of a system, including one or more objects 212 and their associated relationships 214, which may be replicated (see
The horizontal dimension 2004 includes four categories across a range, the first category being least extensible and the fourth category being the most extensible. The categories are: no syndication (the identified system has no ability to share content); individual content syndication (individual items of content within the identified system can be shared); structural subset syndication (structural subsets of the identified system can be shared); and recombinant structures syndication (structural subsets of the identified system can be shared and combined to create new systems). The adaptive recombinant system 800 is capable of all degrees of extensibility depicted in the framework 2000, including the most portable feature, recombinant structures syndication.
The framework 2000 is divided into sixteen numbered blocks, arranged according to their relationship to the horizontal dimension 2002 (degree of adaptiveness) and the vertical dimension 2004 (degree of extensibility). The majority of prior art systems are confined to the lower left portion of the framework 2000. For example, most prior art system are non-adaptive and include no syndication capabilities (block 2010). Current computer operating systems (e.g. Microsoft XP™), business productivity applications (e.g., Microsoft Office™), enterprise applications (e.g., SAP), and search utilities (e.g., Google®) are associated with block 2010 of the framework 2000.
Some prior art systems syndicate items of content or sets of content files. These may be based on a central syndication clearinghouse (e.g., Napster), or may be more purely peer-to-peer in operation (e.g., Gnutella). Such systems are associated with block 2020 of the framework 2000.
Other prior art systems provide users with merchandise recommendations based on their buying habits, as well as the buying habits of customers who have purchased common merchandise (e.g., Amazon.com®). However, these systems do not truly deliver adaptive recommendations as defined herein, whether by displaying adaptive recommendations 250 (block 2050), updating structure or content (block 2090) or providing a continuous, fully adaptive interface (block 2130). This is because, among other reasons, the scope of the usage behaviors tracked by such prior art systems is limited to purchasing and associated behaviors.
In contrast, for the adaptive system 100 and the adaptive recombinant system 800, more generalized system usage behaviors 247 are tracked and used to deliver adaptive recommendations 250 to the user 200 and to the adaptive (recombinant) system itself. Thus, prior art systems such as Amazon.com are deemed non-adaptive (block 2010) in the framework 2000. Blocks 2010 and 2020 of the framework 2000 thus represent the extent of prior art system capabilities with regard to system adaptation (vertical dimension 2002) and portability (horizontal dimension 2004).
In contrast, the adaptive recombinant system 800 includes the adaptability and portability associated with the remaining blocks of the framework 2000. For example, the adaptive recombinant system 800 is capable of syndicating non-adaptive structural subsets 280 of the system 800 (block 2030), as well as syndicating non-adaptive recombinant structures (block 2040). Thus, the adaptive recombinant system 800 exhibits a high degree of extensibility, fully covering the horizontal dimension 2004 of the framework 2000.
The vertical dimension 2002 is likewise embodied both by the adaptive system 100 and the adaptive recombinant system 800. While the adaptive system 100 displays adaptive recommendations 250 where no syndication occurs (block 2050), the adaptive recombinant system 800 further displays adaptive recommendations 250 where individual content is syndicated (block 2060), where structural subsets 280 are syndicated (block 2070) and where recombinant structures are syndicated (block 2080).
Moving up the vertical dimension 2002, the adaptive system 100 provides adaptive recommendations 250 that update the structural aspect 210 and/or the content aspect 230 of the system where there is no syndication (block 2090), and the adaptive recombinant system 800 provides adaptive recommendations that update the structural or content aspects where individual content is syndicated (block 2100), where structural subsets 280 are syndicated (block 2110), and where recombinant structures are syndicated (block 2120).
Finally, the adaptive recombinant system 800 provides a continuous, fully adaptive interface for all four categories of syndication (blocks 2130, 2140, 2150, and 2160) while the adaptive system 100 does so where there is no syndication (block 2130). Thus, the adaptive system 100 and the adaptive recombinant system 800 provide various degrees of adaptiveness and extensibility, as represented in the framework 2000.
Sample Recommendations Function and Algorithm
In this example, two types of adaptive recommendations are delivered to the user. The adaptive recommendations are calculated by a set of algorithms based on the systems objects being currently navigated, the relationships of the currently accessed object, the user's navigation path, profile preferences, community membership and level of relevance depending on context and the user's personal library of referenced objects. Recall that a “user” may refer to not only humans, but to another system or adaptive network. In other words, two or more adaptive systems may be “users” of each other.
Two types of adaptive recommendations based on a fuzzy content network structure are described in Table 4. One skilled in the art may apply other variations of adaptive recommendations and associated algorithms.
TABLE 4 Two Recommendations Algorithms Type Delivery characteristics in-context when user is accessing or may be delivered in real- (suggestions) interacting, accessing, or time updating content object available in display pages for retrieval/editing may be optimized for responsiveness and “fast” learning of user preferences out-of-context no explicit access of inferences may be (recommendations) content object by user updated in real time or periodically available in display pages for retrieval may be optimized for accuracy and understand- ing of user preferences
The first adaptive recommendations type, in-context recommendations, or suggestions, are delivered to the user when the user is interacting, accessing, or updating a content object. In-context recommendations may be delivered in real time, may be displayed for retrieval and editing, and may be optimized for responsiveness and the “fast” learning of the user's preferences.
The second adaptive recommendations type, out-of-context recommendations, is a “push” recommendation approach. Based on inferences about the user's preferences, the network is aligned to adapt to the preferences. The out-of-context recommendations thus “surprise” the user with recommendations of relevant objects of interest without specific explicit context from the user. Relevant characteristics for out-of-context recommendations include the real-time or periodic updating of inferences and the ability to provide adaptive recommendations in display pages or via other modes of communication for retrieval Further, the out-of-context recommendations algorithm may be optimized for accuracy and understanding of user preferences
Adaptive Recommendations Function Example
The following data is used by the adaptive recommendations function 900 in generating recommendations:
The adaptive recommendations function 900 begins by determining personal highest recommendation areas, or PHRAs of the user (block 902). PHRAs are, generated by determining the highest relevance sums of co-topic-community relationships. To illustrate this step, Table 5 includes an abbreviated matrix of topics and communities on one axis versus content objects and topic objects on the other matrix, with numerical relationships between the two axes.
Relationships between objects in fuzzy content network
object 1 (article)
object 2 (presentation)
object 3 (book)
In this limited example, there are three topics, topic A, topic B, and topic C, and one community, community X, that have varying degrees of relationship (rated between 1 and 5) to other objects in the system: object 1 (an article), object 2 (a presentation), object 3 (a book), and topic A. Calculating the highest sum of relationships for the particular context (total row) results in the generation of PHRAs.
In Table 5, topic B and community X have the highest relationship sums thus two PHRAs are found in this example. This method will often generate many PHRAs, which sometimes may be too many to make useful suggestions from. For example, there may be a dozen or more PHRAs with the same value. In this case, the tie breakers are the data that informs on relationships between topics and communities.
For example, in Table 5, topic A has a strong relationship (5) to community X. Topic A itself has a high total score. Thus, the adaptive recommendations function 900 assigns a dynamic weighting to topic A's relevance to community X, to strengthen community X's result. In this case, if it was desirable to have only one PHPA, community X would be chosen. In some embodiments, the top 3-5 PHRAs are selected by the adaptive recommendations function 900.
Building on this procedure, the storing of the dynamic weightings generated in this process can be useful as an additional recommendation mechanism. This approach allows the adaptive recommendations function 900, at the end of processing, to compare which recommendation is actually selected by the user from the top suggestions generated. If there is a discrepancy or convergence, the weightings may be examined and used as a way to strengthen or weaken the relationships between topics, objects and communities for this user's particular context.
The adaptive recommendations function 900 also determines Epiture's highest recommendation area, or EHRA (block 904). Recall that, in the adaptive recombinant system 800, relationships between objects, topics and communities, may be made by experts. There may also be explicit business rules in the system to conform) to, for example in the form of a business process. II addition, the relationship context may be delivered from another fuzzy content network or instance of the adaptive recombinant system, in particular when ‘training’ a new knowledge network or integrating existing networks. The Epiture software system includes these features in determining EHRAs.
A set of Epiture's highest recommendation areas (EHRA) may be generated by selecting related topics or communities with higher relevance values to the current object. The EHRAs are weighted appropriately to the situation, either by system rules, or by user preferences.
The adaptive recommendations function 900 also determines Epiture's highest recommended objects (block 906). Again, this step uses relationships already in existence in the system, either an average across all relationships and quality ratings, or tuned to select a particular set of relationship types or quality ratings. From these data, a set of Epitures highest recommendation objects (EHRO) may be generated by selecting related content objects with higher relevance values (with relevance defined by context of both the object in question and system ‘priorities’) to the current object.
Although steps 902, 904, and 906 are presented in a particular order in
If not, however, the adaptive recommendations function 900 determines whether it can find any objects in EHRO that also exist in the PHRA. If so, those results will be returned and the operation ends even though the selected objects are a second tier of the recommended objects. To ensure that the user realizes this, a relevance weighting may be assigned, and graphically indicated if needed.
A third tier of recommended objects may be found by finding any objects in the EHRO that exist in the EHPA, using quality, relationships types and values and other attributes as guides for making the selection.
If a sufficient set of recommendation objects have been found (the “yes” prong of block 910), the adaptive recommendations function 900 removes duplicated objects in the potential recommendations determined thus far (block 908). This step is particularly relevant where the users of the Epiture software system are human users who have been browsing the system for some time period. Such users generally do not wish to be recommended content they have already read, visited, or used. If the user has already visited some of the selected recommended objects within a predetermined time period, say, in the last 24 hours, or, if some of recommended objects are already in the user's personal topic library, the adaptive recommendations function 900 determines the object to be unnecessary to recommend. Thus, such objects are removed from the recommendation object set.
Where objects removed in this manner cause the available adaptive recommendations to be insufficient or empty (the “no” prong of block 914), or where enough adaptive recommendations were not produced initially (the “no” prong of block 910), the adaptive recommendations function 900 proceeds to determine the most popular jump objects in the path of a community (block 916).
The adaptive recommendations function 900 examines the paths of other users who have browsed the object. Given criteria such as similar community membership to the current user, content quality rating and distribution, overall popularity, and other attributes, it is determined which objects to recommend based on prior usage. This fourth tier of recommendation objects (besides PHRAs, EHRAs, and EHROs) is designated as a second set of Epiture's highest recommended objects or EHRO2.
This step (block 916) may be helpful in the case of integrating two or more networks together. Since the relationship context and attributes of the objects in the network may be ‘carried’ over or ported into th e new network, the objects may ‘look’ for their prior relationships and segment based on usage criteria. In addition, influence and other metrics and attribute patterns may be used to determine similarities between objects. Thus, the adaptive recommendations function 900 may connect objects which have not been in contact before, providing the user a targeted recommendation, and generating a relationship between those objects. That newly formed relationship may cascade to affect other objects in the system such as communities and topics
Finally, the adaptive recommendations function 900 may track usage of adaptive recommendations (block 918). As the embedded algorithms are optimized for speed and real-time performance for in-context recommendations, the ‘understanding’ and true relevance (as inferred from user usage behavior) of the adaptive recommendations may be processed later As such, tracking the selection and usage of adaptive recommendations at this time may be beneficial Criteria such as placement position on a list or other display mechanism, determined (estimated) relevance as predicted by the algorithm versus first selections by the user, and choice of object type (such as article, subject matter expert, multimedia, image etc), are just a few examples of how the adaptive recommendations function may self-monitor its performance. This performance analysis may ultimately generate better quality recommendations for the user, and be used in updating system structure such as EHRA inputs. Or, the system may be self-policing, in effect, making changes as usage data builds up.
It should be noted that the adaptive recommendations function 900 depicted in
The screenshot 770 also depicts a user personal library function 714, denoted “My Personal Topics,” for a particular user. A screenshot 720 in
In the screen image 790 of
Path data can be used to strengthen adaptive recommendations on an automatic basis, while also contributing to input of an automatic or semi-automatic recommendation for the setup of a new community or new topical area.
Cumulative usage data may also be of interest to users of the system as illustrated in the screen image 780 of
Automatic Fuzzy Content Network Maintenance
The adaptive recommendations function and related sets of algorithms, in conjunction with the fuzzy network maintenance functions, may be used to automatically or semi-automatically update and enhance the fuzzy content network. These functions may be employed to determine new affinities and the appropriate degree of relationship among fuzzy network objects in the fuzzy network as a whole, within personal fuzzy network subsets, or sub-community-specific fuzzy network subsets. The automatic updating may include potentially setting a relationship between any two objects to zero (effectively deleting a relationship link).
The recommendation function and fuzzy network maintenance functions may operate completely automatically, performing in the background and updating affinities independently of human intervention, or the function may be used by users or special experts who rely on the adaptive recommendations to provide guidance in maintaining the fuzzy network as a whole, or maintaining specific fuzzy network subsets.
In either an autonomous mode of operation, or in conjunction with human expertise, the recommendation function may be used to integrate new content or content objects into the fuzzy content network.
As in the case of adaptive recommendations that are delivered to recipients to enhance their ability to effectively navigate and use the system, adaptive recommendations that function to update the fuzzy content network include algorithms that make inferences from the usage behaviors of system users. These inferences may be at the community level, sub-community level, or individual user level. Usage behaviors that may be included in the inferencing include online information accesses, traffic patterns and click streams associated with navigating the system structure, including buying and selling behaviors; physical locational information associated with stationary or mobile use of the system; collaborative behaviors among system users or systems users and people outside the system, that include written and oral communications; referencing behaviors of system users—for example, the tagging of information for future reference; subscription and other self-profiling behavior of users; and direct feedback behaviors, such as the ratings or direct written feedback associated with objects or their attributes such as the objects' author, publisher, etc. The algorithms may also use information associated with temporal information associated with usage behaviors, including the duration of behaviors and the timing of the behaviors, where the behaviors may include those associated with reading or writing of written or graphical material, oral communications, including listening and talking, or duration of physical location of a system user.
In some embodiments, inferences regarding a plurality of usage behaviors may be used to adjust relationships and associated relationship values and indicators, as explained in the sample embodiment above. These fuzzy network structural modifications may be applied to multiple relationship types. Navigational access information may be used by the algorithms; that is, the relative level of traffic between two objects (each either a content object or a topic object) will influence the degree of relationship between the two objects. However, access information alone is likely to be insufficient for best results as navigation accesses are highly influenced by the current system structure, and therefore current structures would tend to be reinforced, limiting the level of adaptation. Therefore, other or additional behavioral information is preferentially used to overcome this bias. For example, duration of viewing objects typically provides a better indication of value of an object to a user than does just an object access, as does, for example, reference and reference organization cues, collaboration cues, and direct feedback. Therefore, this additional behavioral information may be used to adjust the strengths of relationships among objects.
As an example, where referenced or tagged information can be organized by users, the system may scan the referenced information and how it is organized, and the frequency of the organizational structures among users, to determine a preliminary degree of relationships in the system. This may be augmented by information associated with navigational accesses and the duration of the accesses.
As a simplified example,
The fuzzy network 670a may have a popular access path 672a from Node X to Node Y which in turn has a popular access path 674a to Node Z. Assuming the existing relationships along that path are of similar strength, it might suggest, without any additional information, that these relationships should perhaps be strengthened due to the high popularity of the path. However, more usage behavioral information may suggest a different fuzzy network updating approach. For example, the duration of accesses of Node X and Node Z were generally significantly higher than for Node Y, a better structural update might be to increase, or establish, the relationship between Node X and Node Z, as is shown in the fuzzy network 670b. After application of an algorithm that incorporates the durational usage behavioral cues, a relationship 676b is established between Node X and Node Z. In addition, in this example, the former relationship 672a between Node X and Node Y is deleted (in practice, it might just be weakened in strength).
The structural transformation from fuzzy network 670a to 670b as shown would be even more reinforced if additional usage behavioral information supported reinforced the access durational-based inferences on preferences. For example, if Node X and Node Z were more frequently referenced by users than Node Y, and were organized such as to imply close affinity (for example, stored in the same personal topical area). This would be more confirming information to strengthen the relationship between Node X and Node Z, and to weaken or eliminate the relationship between Node X and Node Y.
The relationship updating algorithm may temper potential relationship updating, including adding new relationships, with global considerations related to optimal connections among network objects. For example, too few relationships, or relationships with insufficient spread of strength values tend to inhibit effective navigation, but on the other hand too many relationships also is not optimal. The algorithm may strive to maintain an optimal richness of relationships while updating the fuzzy content network based on usage characteristics. The algorithm may use preferential distributions based on fuzzy network metrics such as connectedness and influence to optimize the fuzzy network relationship topologies.
The recommendation function or related algorithms, in conjunction with the fuzzy content network maintenance functions, may also be extended to scan, evaluate, and determine fuzzy network subsets that have special characteristics. For example, the recommendation function or related algorithms may suggest that certain of the fuzzy network subsets that have been evaluated are candidates for special designation. This may include being a candidate for becoming a topical area. The recommendation function may suggest to human users or experts the fuzzy network subset that is suggested to become a topical area, along with existing topical areas that are deemed by the recommendation function to be “closest” in relationship to the new suggested topical area. A human user or expert may then be invited to add a topic, along with associated meta-information, and may manually create relationships between the new topic and existing topics. Statistical pattern matching or learning algorithms used to identify such fuzzy network subsets may include, but are not limited to, semantic network techniques, Bayesian analytical techniques, neural network-based techniques, k-nearest neighbor, support vector machine-based techniques, or other statistical analytical techniques.
The algorithms may apply fuzzy network usage behaviors, along with user community segmentations, to determine new topical areas. The algorithms may be augmented with global considerations related to optimal topologies of fuzzy network structures so as to deliver the most effective usability. For example, too many topics, or topics not sufficiently spread across the over domain of information or knowledge addressed by the system, tend to inhibit effective navigation and use. The algorithm may strive to maintain an optimal richness of topical areas. The algorithm may use preferential distributions based on fuzzy network metrics such as connectedness and influence to optimize the fuzzy network relationship topologies. This approach may also be employed in suggesting topical areas for deletion.
Or, the recommendation function or related algorithms, in conjunction with the fuzzy content network maintenance functions, may automatically generate the topic object and associated meta-information, and may automatically generate the relationships and relationship indicators and their values between the newly created topic object and other topic objects in the fuzzy network.
In some embodiments this capability may be extended such that the recommendation function or related algorithms, along with fuzzy network maintenance functions, automatically maintain the fuzzy network and identified fuzzy network subsets. The recommendation function may not only identify new topical areas, generate associated topic objects, associated relationships and relationship indicators among the new topic objects and existing topic objects, and the associated values of the relationships indicators, but also identify topic objects that are candidates for deletion, and in some embodiments may automatically delete the topic object and its associated relationships.
The adaptive recommendations function, in conjunction with the fuzzy network maintenance functions, may likewise identify content objects that are candidates for deletion, and may automatically delete the associated content objects and their associated relationships.
In this way the adaptive recommendations function or related algorithms, along with the fuzzy content network maintenance functions, may automatically adapt the structure of the fuzzy network itself on a periodic or continuous basis to enable the best possible experience for the fuzzy network's users.
As in network embodiments, when a new fuzzy content network is initialized, the adaptive recommendation function may also serve as a training mechanism for the new network. Given a distribution of content, relationships and relationships types, metrics and usage behaviors associated with scope, subject and other experiential data of other fuzzy content networks, a module of the adaptive recommendation function may automatically begin assimilation of content objects into a fuzzy content network, with intervention as required by humans. Clusters of newly assimilated content objects may enable inferences resulting in the suggestion of new topical objects and communities, and associated relationship types and indicators may also be automatically created and updated. This functionality of the adaptive recommendation engine may also be applied when two or more fuzzy content networks are brought together and require integration.
Each of the automatic steps listed above may be interactive with human users and experts as desired.
Social Network Analysis in Fuzzy Content Object Networks
Social network analysis may be conducted with adaptive recombinant system 800 in multiple ways. First, the representation of a person or people may be explicitly through content objects in the fuzzy content network. Special people-type content objects may be available, for example. Such a content object may have relevant meta-information such as an image of the person, and associated biography, affiliated organization, contact information, etc. The content object may be related to other content objects that the person or persons personally contributed to, topics that they have particular interest or expertise in, or any other system objects with which the person or persons have an affinity. Tracking information associated with access to these content objects by specific users, and/or user sub-communities may be determined as described above.
Furthermore, collaborative usage patterns may be used to understand direct communications interactions among persons, in addition to indirect interactions (e.g., interactions related to the content associated with a person). The physical location of people may be tracked, enabling an inference of in-person interactions, in addition to collaborations at a distance.
Second, specific people may be associated with specific content and topic objects—for example, the author of a particular content object. These people may or may not have explicit associated people-type content objects. Metrics related to the popularity, connectedness, and influence of a person's associated content may be calculated to provide measurement and insights associated with the underlying social network. The associations with content objects may be with a group of people rather than a single individual such as an author. For example, the metrics may be calculated for organizations affiliated with content objects. An example is the publisher of the associated content.
In either of the approaches described above, report-based and graphical-based formats may be used to display attributes of the underlying social network. These may include on-line or printed displays that illustrate how communities or sub-communities of users directly access a set of people (through the associated content objects), or indirectly through associated content affiliated with the set of people.
Adaptive Processes and Process Networks
The adaptive system 100 and the adaptive recombinant system 800 enable the effective implementation of computer-based or computer-assisted processes. Processes involve a sequence of activity steps or stages that may be explicitly defined, and such sequences are sometimes termed “workflow.” These processes may involve structures that require, or encourage, a step or stage to be completed before the next step or stage may be conducted. Additional relevant details on process-based applications and implementations of adaptive networks is disclosed in U.S. Provisional Patent Application, No. 60/572,565, entitled “A Method and System for Adaptive Processes,” which is incorporated herein by reference, as if set forth in its entirety.
A set of relationships and associated relationship indicators may be employed to designate process flows among objects in a fuzzy network, or fuzzy content network. The existence of a process relationship between object x and object y implies that x precedes y in a specified process. A process relationship may exist between object x and a plurality of other objects. In these embodiments, a user may have a choice of multiple process step options from an originating process step. The values of a plurality relationship indicators associated with the process relationships between an object and a plurality of objects may be different.
A plurality of process relationship indicators may be designated among the objects in a fuzzy content network, which enables objects to be organized in a plurality of processes.
Display functions enable a user to navigate through a fuzzy network or fuzzy network subset via objects that have process relations between them. At each process step, corresponding to accessing the corresponding object, the user may have the ability to navigate to other related objects, which can be advantageous in providing the user with relevant information to facilitate executing the corresponding process step.
Fuzzy processes may be organized into fuzzy sub-processes through selection of a subset of objects corresponding to a contiguous set of process steps, along with all other objects related to the process step objects, or more generally, as the set of all objects within a specified fractional degrees of separation from each of the process step objects.
New fuzzy processes may be generated by combining fuzzy process sub-networks into new fuzzy process networks using the fuzzy network union, intersection and other operators.
Computing system 966 represents a PC or other computing system which connects through a gateway or other host in order to access the server 952 on which the systems 100 or 800 reside. An appliance 968, includes software “hardwired” into a physical device, or may utilize software running on another system that does not itself host the systems 100 or 800. The appliance 968 is able to access a computing system that hosts an instance of the system 100 or 800, such as the server 952, and is able to interact with the instance of the system 100 or 800. P While the present invention has been described with respect to a limited number of embodiments, those skilled in the art will appreciate numerous modifications and variations therefrom. It is intended that the appended claims cover all such modifications and variations as fall within the true spirit and scope of this present invention.
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