CA1324675C - System and method of predicting subjective reactions - Google Patents
System and method of predicting subjective reactionsInfo
- Publication number
- CA1324675C CA1324675C CA000609923A CA609923A CA1324675C CA 1324675 C CA1324675 C CA 1324675C CA 000609923 A CA000609923 A CA 000609923A CA 609923 A CA609923 A CA 609923A CA 1324675 C CA1324675 C CA 1324675C
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
Abstract
Applicant: John B. Hey For: System and Method of Predicting Subjective Reactions ABSTRACT OF THE DISCLOSURE
A system and method of predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user. The predictions are based on other items previously sampled by that user. A scalar rating is defined for each item sampled by the selected user to represent the reaction of the selected user to that item. The selected user is successively paired with other users in the group who have defined scalar ratings for at least some of the items sampled by the selected user to determine the overall difference in ratings for items sampled by both members of each successive pair. One or more of the other users are designated as predicting users and a weighting value is assigned to each of the predicting users based on the overall difference in ratings between that predicting user and the selected user. The weighting values are applied for each item unsampled by the selected user to proportionally alter the difference between a rating previously predicted for each identified item and any actual ratings of that item by the predicting users to adjust the reaction predictions for the selected user.
A system and method of predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user. The predictions are based on other items previously sampled by that user. A scalar rating is defined for each item sampled by the selected user to represent the reaction of the selected user to that item. The selected user is successively paired with other users in the group who have defined scalar ratings for at least some of the items sampled by the selected user to determine the overall difference in ratings for items sampled by both members of each successive pair. One or more of the other users are designated as predicting users and a weighting value is assigned to each of the predicting users based on the overall difference in ratings between that predicting user and the selected user. The weighting values are applied for each item unsampled by the selected user to proportionally alter the difference between a rating previously predicted for each identified item and any actual ratings of that item by the predicting users to adjust the reaction predictions for the selected user.
Description
~32467~J
Applicants John B. ~ey Fors System ~nd Method of Predictlng SubjectlYe ~eactlons ~=
Th~ invention relate~ to a system and method of predicting reactions to itemfi not yet ~ampled by a user, and more particularly to 6uch a system and method which adjust the reaction prediction for each unsampled item for that user based on the similarity in reaction of other user6 relative to that user.
BACRGROI~ND OP INVENTIOl~
There are a number of situations in which it is helpful to predict the reactions of people to items they have not yet had the opportunity to sample. It is particularly useful to make recommendations for items to which people have wholly subjective reactions and which require a substantial investment of time or money to review, such ac movies, books, mu~ic, and games.
Difficulty arises because the actual reaction of a person to such an item can only be determined after money and time are invested in sampling the item.
The desirability of making recommendations for cubjectively appreciated item~ is evidenced by the prevalence of movie critics, book reviewers, and other critics who attempt to appraise such items. ~owever, the uniqueness of each item ~32~
hinde~6 objective compari~on of the items relative to the re~ponse they will eliclt from each lndivldual. Short ~ynopses or reviews are of limited value bec~use the actual 6atisfactlon of an indlvldual depend6 upon hl6 reactlon to the entire rendition of the item. For example, book6 or mov~es with very similar plots can differ widely in style, pace, mood, and other characteristic6. Moreover, knowledge beforehand of the plot or content can lessen enjoyment of the item.
It is common to 6tudy the advice of profes~ional critics, but it is difficult at best to find a critic whose taste matches the taste of a particular individual. U~ing a combination of critics provides more information, but correctly combining and interpreting multiple opinions to extract useful advice i~ quite difficult. Even if a satisfactory combination is achieved, the opinion~ of profes6ional critics frequently change over time as the critics lose their enthusiasm or become overly sophisticated.
Public opinion poll~ attempt to discern the average or majority opinion on particular topics, particularly for current events, but by their nature the polls are not tailored to the subjectlve opinions of any one person. Poll~ draw from a large amount of data but are not capable of responding to the subjective nature of a particular individual.
All of the above techniques requlre research by an individual, and the research is time consuming and often applied to out-of-date material. An individual is provided little help ~ 32~7~
in making an opt~mal choice from a l~rge ~et of largely unknown item6.
~UMMARY OF INVENTION
It is therefore an object of this invent~on to provide a system and method which automatically and accurately pred~ct the subjective reaction of a person to ~tems not yet ~ampled by that person.
It i~ a further object of this invention to provide such a system and method which draw upon the experience of a group of people and selectively weight the reactions of the group to make accurate predictions for any person within the group.
It is a further object of this invention to provide such a system and ~ethod which can repeatedly update the predictions for each person as the experience of the group increases.
Yet another obiect of this invention is to provide a system and method which can evaluate a large number of items and accurately supply an individual with a list of recommendations tailored for that individual.
It i8 i further object of this invention to provide such a system and method which can identify items already sampled and prevent accidental repetition of sampled items.
A still further object of thi6 invention is to provide such a system and method which require little time or effort on the part of each person in a group to obtain accurate recommendations.
~32~rl -' Another object of thi~ invention ifi to p~ovide such a sy~tem ~nd method whlch readily a6 imilate a new person or ltem and rapidly attain a u eful level of predlct~billty for each.
Thi8 ~nvention regult6 from the realization that truly effective prediction of subject1ve reactions, of one or more person~ selected from a group of persons, to unsampled items 6uch as movies, books or mu~ic; can be ach~eved by defining a scalar rating to represent the reaction of the selected person to each sampled item, successively pairing each selected person with other persons in the group to determine the difference in ratings for items sampled by both members of the pair, designating one or more of the other persons as predicting persons, assigning a weighting value to each of the predicting persons, and applying the weighting values to update the ratings previously predicted for each item unsampled by the selected person.
This invention features a method of predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one oe ~ore user~ in the group but not sampled by that user. The predictions are based on other items previously sampled by that user. A scalar rating is defined for each item sampled by the ~elected user to represent the reaction of the selected user to that ite~. The selected user is successively paired with other users in the group who have defined scalar ratings for at least some of the items sampled by the selected user to determine the overall difference in ratings 132~6 ~
for ~tems ~mpled by both me~bers of each succe86i~e pair. One or more of the other user~ are deslgnated a8 pred~cting user6 and a weighting value i~ assigned to each of the predictlng users based on the over~ll difference in rat~ngs between th~t predicting user and the selected user. Items un6ampled by the ~elected user are identif~ed and the weighting values are applied to proportionally alter the difference between a rating previou~ly predicted for each identified item and any actual ratings of that item by the predicting users to adjust the reaction pred~ctions for the selected user.
In one embodiment, successively pairing includes generating for each pair an agreement scalar representing the overall rating difference between the members of that pair, and the weighting value is obtained for each of the predicting users by converting the agreement scalar into the weighting value.
Pairing further includes successively matching, for each pair, items sampled by both members and, for each matched item, subtractinq the ratings of one member from the ratings of the other to obtain the difference in ratings. The difference in ratings for each ~atched item is converted to a closeness value and ~ummed with other closeness value~ for that pair. The sum of the closeness values is weighted by the number of items sampled by both members to generate the agreement scalar.
In another embodiment, designating and converting includes defining a greater weighting value for predicting users havinq a larger agreement scalar and defining a lesser weighting value ~324~
for predlctlng u~er6 having ~ smaller agree~ent Bcalar. The design~ting ~nd converting may ~nclude ranking the predlct$ng u6ers by sscending order of agreement scalar and defining 6uccessively larger weighting values for the ascending agreement scalar~. Identifying and applying includes combining tbe weight$ng value for each predictln~ user wlth the difference between ratings by that predicting user and by the selected user for each ~dentified item, and summing the combination with the previously predicted rating. The pairing may include successively pairing the selected users with each other user in the group or with a subset of other users in the group~ The re~ainder of the users in the group may be successively selected to adjust the reaction predictions for each user in the group.
This invention also features a method of selectively recommending, or disrecommendinq, for each user successively selected from a group of users, items not sampled by the selected u&er. The method includes defining a scalar rating for each sampled item, successively pairing the selected user with other users in the group, generating for each pair an agreement scalar, designating at least one of the other users as recommending users, converting the agreement scalar for each of the recommending use~C into a reco~mendation-fraction, identifying items unsampled by the selected user, and applying the recommendation-fractions to proportionally decrease the difference between a rating previously establi~hed for each identified item and the ratings of that item by the recommending i32~6~
user6 to ad~u6t the recommendations or th~ selected user. The method further includes ~uccesslvely 6electing the remainder of the u~er6 in the group to adju6t the recommendat~ons for e~ch user in the group and presenting, for each user, a plurallty of items based on the recommendation6 for that user.
Thi~ invention further features a sy6te~ for predicting the reaction to items, including means for defining a ~calar rating, means for succe~sively pairing the selected user with other users in the group to determine the difference in ratings, means for de ignating at least one of the other users as a predicting user, and means for assigning a weighting value to each of the predicting usets based on the difference in ratings between that predicting user and the selected user. The system further includes means for applying the weighting Yalues to items unsampled by the selected ucer to proportionally alter the difference between a rating previously predicted for each identified item and any actual rating of that item by the predicting user6 to adjust the reaction predictions for the selected user.
~OSURE OF PREFERRED EM80D.I~.E;NT
Other objects, features and advantages ~ill occur from the following description o a preferred embodi~ent and the accompanying drawings, in which:
Fig. 1 i6 a schematic block diagram of a system according to thi6 invention~
~ 3 2 ~ ~ 7 ~
Fig. 2 i~ a flow chart of the use of the ~yste~ of ~ig. 1 by a u~er~
Fig. 3 i6 a flow chart of the oper~tion of the ~ystem of Fig. 1 for each u6er ~elected to be updated~
Fig. ~ i8 ~ nore det~led flow chart of the p~iring of user6 to determine the difference in rating~ and to generate an agreement scalar; and Fig. S is ~ flow chart of the conver~ion of the agreement scalar to a recommendation-fraction and subsequent adjustment of the previously established rating of the selected person.
This invention may be accomplished by a ~ystem which predicts the reaction of a person selected from a group of persons to items not sampled by the selected person. Th e selected person designates, for each item sampled by the selected person, 2 scalar rating representing the reaction of the selected person to that item. The system ~uccessively pairs the selected person with other persons in the group who have defined scalar ratings for at least some of the items also sampled by the selected person to determine the difference in rating6 for items sampled by both members of each successive pair. The 6ystem further designates one or more of the other persons as a predicting person and assigns a weighting value to each of the predicting per~ons based on the difference in ratings between that predicting person and the selected person.
The weight;ng value i8 applied to items unsampled by the 324~7~rj ~elected person to proport~on~lly alter the difference between the ratlng prevlou ly pcedicted for the ~elected per~on for each unsampled item and the r~tlngs of that item by the predlctlng persons to ~d~ust the overall reaction predictions for the selected person.
Sy~tem 10 according to this invention, Pig. 1, lncludes keyboard 12 through which users of ~ystem 10 enter scalar rating~ for items they have sampled. The ratings are ~tored in memory 14 and are selectively retrieved by pairinq module 16 which, for each per~on for which a prediction is desired, pairs that person with a number of other persons who have previously entered scalar ratings.
A value for each pair representing the difference in ratings for items sampled by both ~ember~ of each successive pair is provided to weighting module I8. For persons designated as predicting person~ for the selected person, as described in more detail below, a weighting value i~ assigned based on the difference in rating~ between that predicting person and the selected person. The weighting values are provided to prediction adju~tment module 20 which applies the weighting values to items unsampled by the selected person to proportionally alter the difference between a rating previously predicted for the selected person for each unsampled item and the ratings of that item by the predicting persons. The rating previously predicted for each un~ampled item represents the predicted reaction of the selected person to the up-to-now -- 132~7r;
un~ampled ~tem. After ~djustment, the ratings are provided to ~emory 1~ ~hich, when re~ue~ted by ~ user, supplles to display 22 a list of u~ually the most highly recommended items for that user. Altern~t$vely, another list ba~ed on the recommendationg i8 provided 6uch a~ a list of tho most highly disrecommended items.
The interface between the user and ~ystem 10 i6 illustrated in Fig. 2. The user enter6 a pa~sword, step 30, and then decides to rate an item, such as a movie, step 32. To rate an item, the name of the item, such as the title of a movie, is entered into the system, ~tep 34. If the item has been previously sampled, step 36, his previous actual rating of it is displayed, step 38. Regardless of whether the item has been actually rated, the user is allo~ed to adju~t the rating, steps 40 and ~2. Increasing the number of items actually sampled and rated increases the accuracy of reaction predictions made for other items a~ explained in greater detail below.
Tn one construction, the scalar ratings are integers ranging from O to 12, with 0~ representing a reaction of ~pooc~, '3~ representing the reaction of 'fair~, ~6~
corresponding to a reaction of ~good~, ~9~ representing the reaction of ~very good~, and ~12~ corresponding to a reaction of ~excellent~. Establishing a greater number of ratings than the above-listed five verbal descriptions provides more accurate rating of the reactions of the user.
- ~2~7~
After the r~ting i6 entered, or ~f adjustment ~8 decllned, the operatlon return6 to tep 32. If rating of an ltem i6 not selected, the u6er elects to view the mo6t current list of recommendation6, ~teps 44, ~6, or ex~t~ the sy~tem, step ~8.
The operation of sy~tem 10, Flg. 1, i~ summarized ln Fig.
3. Each user enters A Bcalar rating for each item sampled by that user, step S0. Each user i~ succe~sively paired, step S2, with a number of other userfi to determine the difference in rating~ for items sampled by both users. For each pair of users, an aqreement scalar i8 generated, step 54, to represent the overall rating difference between that pair of users. For each selected user, one or more of the other users are designated as recommending users, ~tep 56, who contribute to ratings used to make recommendations for items to be sampled.
The agreement scalar for each recommending user is then converted into a recommendation-fraction, step 58, which i~ then applied to reduce the difference between the rating previously estimated for each unsampled item and the actual ratings of that item by the recommending users, ~tep 60. The recon,mendation-fraction is typically a fraction ranqing from zero to one.
The pairing of users to determine the difference in ratings and to generate an agreement scalar is shown in more detail in Fig. ~. For each pair of users, the item is set to first item, step 70, and loop 72 is entered until each of all 1 3 2 4L ~ ~ ~r pos6~ble item h~s been examined. The lte~ ls rec~lled, step 7~, and the ~tem~ for both members of the p~ir are Datched to ee lf that ~tem wa~ sampled by both me~bers, ~tep 76. A numb~r of rating6 for movles are provided ~6 an e~ample in Table Is ~LE Is RA~I~Ç~
Movie ~itle Smit~ Jones Wesso~
Star War~ 8 11 10 ~he Untouchables 10 9 4 Beverly ~ills Cops - 10 10 Fletch 10 - g Caddyshack 7 - 11 qhe rating difference is determined, step 78, and a closeness value is obtained for that difference, step 80. In one construction, the closeness value is obtained from a look-up table such as Table II:
132~7v WLE I I: I~Ti NG ~0 C~LOSE~ SS-VAhyE
jf~4 C~oliene6B V~l o 10 Step 80 provides a weighting step in which large difference~ in ratings are penalized and similarities are rewarded. In other constructions, the unaltered differences themselves are used.
In yet other embodiment~, ratios or item-specific probabilities of the differences may be compared, or agreement by types or categories of items may be utilized.
In this embodiment, the clo~ene6s-value i8 added to ~
running total, step 82, and the count of ite~ sampled by both members is incremented, step 8~. After the last item has been processed, step 86, an aqreemert ~calar is generated, step 88, 132467r for that palr of user6. ~he agreement scalar may be gener~ted by the use of the followlng equations AS - (CV~) ~2n-1)/n2 ~1) where AS i8 the agreement scalar, CVT i6 the closeness-value total, and n i6 the count o items sampled by both users. By the example provided in Tablç~ I and II, Smith and Jone~ have sampled two items in common having a difference in ratings of 3 and 1, respectively, which are assigned closeness values of 4 and 9, respectively. By application of equation (1), the agreement scalar for Smith and Jones is 9.75. Similarly, the closeness-value for the pair of Smith and Wesson is 17 and the agreement scalar is 7.44. The difference in reaction of Smith and Wesson to ~The Untoucbables~ and ~Caddyshack~ led to the smaller agreement scalar between those users. It is evident that the greater the number of items that the users have sampled, the more accurate the agree~ent scalar will be for each of the users with which the selected user i8 paired.
The conversion of the agreement scalar to a weighting value, referred to as a recommendation-fraction, and ad~ustment of the previously establi~hed ratings i6 shown in Fig. 5. One or more recommending users are designated, step 90, from the group of users. ~f the number of users is small, the entire group may be used. Otherwise, a subset of the group, e.g.
sixteen users may be used. Succe~sive ones of the users are 1 32~ ~7~i de6ign~ted as ~elected use~ wh~le the remainder of the ~ub~et are deslqnated as recommending user6.
The recommend~ng u6ers are ranked by order of ~greement scalar, 6tep 92. ~ach recommending user i6 then utilized to adjust the previou61y e tabll~hed predicted ratings for tbe selected user, loop ~ A recommendation-fractlon i8 defined for the agreement Ecalar of the first recommending user, step 94. It i8 desirable to rank the recommending person6 by ascending order of agreement scalar and in that order as6igning to the ranked predicting person~ progres6ively larger weighting values. In one embodiment, for the fourth highest agreement 6calar a recommendation-fraction of 1/16 is defined, for the third highest a recommendation fraction of 1/8 is defined, for the second highest a recommendation-fraction of 1/4 i defined, and for the highest a recommendation-fraction of 1/2 is defined.
All other agreement scalars are assigned a value of zero or their recon,mendation-fraction. The lists of items for the recommending user and the selected user are matched to identify, step 96, items 6a~pled by the recommending user but not by the selected user. Each identifying item is analy2ed in loop 98 in which the difference between ratings is determined, the recommendation-fraction and the difference are combined, and the rating is adjusted by the combination, steps 100, 102, 104, respectively. When the recommendation-fraction has been combined with the difference for each item including the last 1 3 2 4 ~ 7 ) ldentified item, 6tep 106, the next recommending u~er 18 selected, ~tep 108.
In one embodiment, a difference between the ratinqs 18 determined by subtracting the prevlou~ly estlmated rat1ng of the selected user from the actual eating of the recommending user.
The difference i8 then multiplled by the recommendation-fraction to obtain an adjustment, and the adjustment $6 added to the previou61y e6timated rating. When the recommending user6 are ranked in order of lowest to highest agreement scalar, the relative adjustment accorded by the recommending user with the highest scalar i~ enhanced. That is, his weighting effect is not diluted by later adjustments from less appropriate recommending users.
While the terms ~person~ and ~user~ as used above refer to a human being, the terms are used in their broadest sense to refer to any entity which exhibits a subjective but not random reaction to an item. The above-de6cribed sy~tem and method of operation according to the present invention similarly apply to more than movieæ, record albums, computer games, ol other consumer items. For example, reaction6 can be predicted for travel destination~, hotels, restaurants, or career6. Further, predlctions among categories can be accomplished, e.g., recommending books based on the ratings of movies. The system and method according to this invention are particularly useful for items which have significance in and of themselves to people, that is, predicting the reactions of people to the items ., 1324~7r~
benefits the people in optimally d~rectlng their lnve~ment of time and money ln choosing and 6ampling itecs.
Although sp4eific feature6 of the lnvention ale 6hown ln some drawing6 and not other~, thls i6 for convenience only a~
each feature ~ay be combined with any or all of the other feature6 in accoraance with the invention.
Other embodi~ent6 will occur to tho6e skilled in the art and are with$n the following claim6:
What i6 claimed i6:
Applicants John B. ~ey Fors System ~nd Method of Predictlng SubjectlYe ~eactlons ~=
Th~ invention relate~ to a system and method of predicting reactions to itemfi not yet ~ampled by a user, and more particularly to 6uch a system and method which adjust the reaction prediction for each unsampled item for that user based on the similarity in reaction of other user6 relative to that user.
BACRGROI~ND OP INVENTIOl~
There are a number of situations in which it is helpful to predict the reactions of people to items they have not yet had the opportunity to sample. It is particularly useful to make recommendations for items to which people have wholly subjective reactions and which require a substantial investment of time or money to review, such ac movies, books, mu~ic, and games.
Difficulty arises because the actual reaction of a person to such an item can only be determined after money and time are invested in sampling the item.
The desirability of making recommendations for cubjectively appreciated item~ is evidenced by the prevalence of movie critics, book reviewers, and other critics who attempt to appraise such items. ~owever, the uniqueness of each item ~32~
hinde~6 objective compari~on of the items relative to the re~ponse they will eliclt from each lndivldual. Short ~ynopses or reviews are of limited value bec~use the actual 6atisfactlon of an indlvldual depend6 upon hl6 reactlon to the entire rendition of the item. For example, book6 or mov~es with very similar plots can differ widely in style, pace, mood, and other characteristic6. Moreover, knowledge beforehand of the plot or content can lessen enjoyment of the item.
It is common to 6tudy the advice of profes~ional critics, but it is difficult at best to find a critic whose taste matches the taste of a particular individual. U~ing a combination of critics provides more information, but correctly combining and interpreting multiple opinions to extract useful advice i~ quite difficult. Even if a satisfactory combination is achieved, the opinion~ of profes6ional critics frequently change over time as the critics lose their enthusiasm or become overly sophisticated.
Public opinion poll~ attempt to discern the average or majority opinion on particular topics, particularly for current events, but by their nature the polls are not tailored to the subjectlve opinions of any one person. Poll~ draw from a large amount of data but are not capable of responding to the subjective nature of a particular individual.
All of the above techniques requlre research by an individual, and the research is time consuming and often applied to out-of-date material. An individual is provided little help ~ 32~7~
in making an opt~mal choice from a l~rge ~et of largely unknown item6.
~UMMARY OF INVENTION
It is therefore an object of this invent~on to provide a system and method which automatically and accurately pred~ct the subjective reaction of a person to ~tems not yet ~ampled by that person.
It i~ a further object of this invention to provide such a system and method which draw upon the experience of a group of people and selectively weight the reactions of the group to make accurate predictions for any person within the group.
It is a further object of this invention to provide such a system and ~ethod which can repeatedly update the predictions for each person as the experience of the group increases.
Yet another obiect of this invention is to provide a system and method which can evaluate a large number of items and accurately supply an individual with a list of recommendations tailored for that individual.
It i8 i further object of this invention to provide such a system and method which can identify items already sampled and prevent accidental repetition of sampled items.
A still further object of thi6 invention is to provide such a system and method which require little time or effort on the part of each person in a group to obtain accurate recommendations.
~32~rl -' Another object of thi~ invention ifi to p~ovide such a sy~tem ~nd method whlch readily a6 imilate a new person or ltem and rapidly attain a u eful level of predlct~billty for each.
Thi8 ~nvention regult6 from the realization that truly effective prediction of subject1ve reactions, of one or more person~ selected from a group of persons, to unsampled items 6uch as movies, books or mu~ic; can be ach~eved by defining a scalar rating to represent the reaction of the selected person to each sampled item, successively pairing each selected person with other persons in the group to determine the difference in ratings for items sampled by both members of the pair, designating one or more of the other persons as predicting persons, assigning a weighting value to each of the predicting persons, and applying the weighting values to update the ratings previously predicted for each item unsampled by the selected person.
This invention features a method of predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one oe ~ore user~ in the group but not sampled by that user. The predictions are based on other items previously sampled by that user. A scalar rating is defined for each item sampled by the ~elected user to represent the reaction of the selected user to that ite~. The selected user is successively paired with other users in the group who have defined scalar ratings for at least some of the items sampled by the selected user to determine the overall difference in ratings 132~6 ~
for ~tems ~mpled by both me~bers of each succe86i~e pair. One or more of the other user~ are deslgnated a8 pred~cting user6 and a weighting value i~ assigned to each of the predictlng users based on the over~ll difference in rat~ngs between th~t predicting user and the selected user. Items un6ampled by the ~elected user are identif~ed and the weighting values are applied to proportionally alter the difference between a rating previou~ly predicted for each identified item and any actual ratings of that item by the predicting users to adjust the reaction pred~ctions for the selected user.
In one embodiment, successively pairing includes generating for each pair an agreement scalar representing the overall rating difference between the members of that pair, and the weighting value is obtained for each of the predicting users by converting the agreement scalar into the weighting value.
Pairing further includes successively matching, for each pair, items sampled by both members and, for each matched item, subtractinq the ratings of one member from the ratings of the other to obtain the difference in ratings. The difference in ratings for each ~atched item is converted to a closeness value and ~ummed with other closeness value~ for that pair. The sum of the closeness values is weighted by the number of items sampled by both members to generate the agreement scalar.
In another embodiment, designating and converting includes defining a greater weighting value for predicting users havinq a larger agreement scalar and defining a lesser weighting value ~324~
for predlctlng u~er6 having ~ smaller agree~ent Bcalar. The design~ting ~nd converting may ~nclude ranking the predlct$ng u6ers by sscending order of agreement scalar and defining 6uccessively larger weighting values for the ascending agreement scalar~. Identifying and applying includes combining tbe weight$ng value for each predictln~ user wlth the difference between ratings by that predicting user and by the selected user for each ~dentified item, and summing the combination with the previously predicted rating. The pairing may include successively pairing the selected users with each other user in the group or with a subset of other users in the group~ The re~ainder of the users in the group may be successively selected to adjust the reaction predictions for each user in the group.
This invention also features a method of selectively recommending, or disrecommendinq, for each user successively selected from a group of users, items not sampled by the selected u&er. The method includes defining a scalar rating for each sampled item, successively pairing the selected user with other users in the group, generating for each pair an agreement scalar, designating at least one of the other users as recommending users, converting the agreement scalar for each of the recommending use~C into a reco~mendation-fraction, identifying items unsampled by the selected user, and applying the recommendation-fractions to proportionally decrease the difference between a rating previously establi~hed for each identified item and the ratings of that item by the recommending i32~6~
user6 to ad~u6t the recommendations or th~ selected user. The method further includes ~uccesslvely 6electing the remainder of the u~er6 in the group to adju6t the recommendat~ons for e~ch user in the group and presenting, for each user, a plurallty of items based on the recommendation6 for that user.
Thi~ invention further features a sy6te~ for predicting the reaction to items, including means for defining a ~calar rating, means for succe~sively pairing the selected user with other users in the group to determine the difference in ratings, means for de ignating at least one of the other users as a predicting user, and means for assigning a weighting value to each of the predicting usets based on the difference in ratings between that predicting user and the selected user. The system further includes means for applying the weighting Yalues to items unsampled by the selected ucer to proportionally alter the difference between a rating previously predicted for each identified item and any actual rating of that item by the predicting user6 to adjust the reaction predictions for the selected user.
~OSURE OF PREFERRED EM80D.I~.E;NT
Other objects, features and advantages ~ill occur from the following description o a preferred embodi~ent and the accompanying drawings, in which:
Fig. 1 i6 a schematic block diagram of a system according to thi6 invention~
~ 3 2 ~ ~ 7 ~
Fig. 2 i~ a flow chart of the use of the ~yste~ of ~ig. 1 by a u~er~
Fig. 3 i6 a flow chart of the oper~tion of the ~ystem of Fig. 1 for each u6er ~elected to be updated~
Fig. ~ i8 ~ nore det~led flow chart of the p~iring of user6 to determine the difference in rating~ and to generate an agreement scalar; and Fig. S is ~ flow chart of the conver~ion of the agreement scalar to a recommendation-fraction and subsequent adjustment of the previously established rating of the selected person.
This invention may be accomplished by a ~ystem which predicts the reaction of a person selected from a group of persons to items not sampled by the selected person. Th e selected person designates, for each item sampled by the selected person, 2 scalar rating representing the reaction of the selected person to that item. The system ~uccessively pairs the selected person with other persons in the group who have defined scalar ratings for at least some of the items also sampled by the selected person to determine the difference in rating6 for items sampled by both members of each successive pair. The 6ystem further designates one or more of the other persons as a predicting person and assigns a weighting value to each of the predicting per~ons based on the difference in ratings between that predicting person and the selected person.
The weight;ng value i8 applied to items unsampled by the 324~7~rj ~elected person to proport~on~lly alter the difference between the ratlng prevlou ly pcedicted for the ~elected per~on for each unsampled item and the r~tlngs of that item by the predlctlng persons to ~d~ust the overall reaction predictions for the selected person.
Sy~tem 10 according to this invention, Pig. 1, lncludes keyboard 12 through which users of ~ystem 10 enter scalar rating~ for items they have sampled. The ratings are ~tored in memory 14 and are selectively retrieved by pairinq module 16 which, for each per~on for which a prediction is desired, pairs that person with a number of other persons who have previously entered scalar ratings.
A value for each pair representing the difference in ratings for items sampled by both ~ember~ of each successive pair is provided to weighting module I8. For persons designated as predicting person~ for the selected person, as described in more detail below, a weighting value i~ assigned based on the difference in rating~ between that predicting person and the selected person. The weighting values are provided to prediction adju~tment module 20 which applies the weighting values to items unsampled by the selected person to proportionally alter the difference between a rating previously predicted for the selected person for each unsampled item and the ratings of that item by the predicting persons. The rating previously predicted for each un~ampled item represents the predicted reaction of the selected person to the up-to-now -- 132~7r;
un~ampled ~tem. After ~djustment, the ratings are provided to ~emory 1~ ~hich, when re~ue~ted by ~ user, supplles to display 22 a list of u~ually the most highly recommended items for that user. Altern~t$vely, another list ba~ed on the recommendationg i8 provided 6uch a~ a list of tho most highly disrecommended items.
The interface between the user and ~ystem 10 i6 illustrated in Fig. 2. The user enter6 a pa~sword, step 30, and then decides to rate an item, such as a movie, step 32. To rate an item, the name of the item, such as the title of a movie, is entered into the system, ~tep 34. If the item has been previously sampled, step 36, his previous actual rating of it is displayed, step 38. Regardless of whether the item has been actually rated, the user is allo~ed to adju~t the rating, steps 40 and ~2. Increasing the number of items actually sampled and rated increases the accuracy of reaction predictions made for other items a~ explained in greater detail below.
Tn one construction, the scalar ratings are integers ranging from O to 12, with 0~ representing a reaction of ~pooc~, '3~ representing the reaction of 'fair~, ~6~
corresponding to a reaction of ~good~, ~9~ representing the reaction of ~very good~, and ~12~ corresponding to a reaction of ~excellent~. Establishing a greater number of ratings than the above-listed five verbal descriptions provides more accurate rating of the reactions of the user.
- ~2~7~
After the r~ting i6 entered, or ~f adjustment ~8 decllned, the operatlon return6 to tep 32. If rating of an ltem i6 not selected, the u6er elects to view the mo6t current list of recommendation6, ~teps 44, ~6, or ex~t~ the sy~tem, step ~8.
The operation of sy~tem 10, Flg. 1, i~ summarized ln Fig.
3. Each user enters A Bcalar rating for each item sampled by that user, step S0. Each user i~ succe~sively paired, step S2, with a number of other userfi to determine the difference in rating~ for items sampled by both users. For each pair of users, an aqreement scalar i8 generated, step 54, to represent the overall rating difference between that pair of users. For each selected user, one or more of the other users are designated as recommending users, ~tep 56, who contribute to ratings used to make recommendations for items to be sampled.
The agreement scalar for each recommending user is then converted into a recommendation-fraction, step 58, which i~ then applied to reduce the difference between the rating previously estimated for each unsampled item and the actual ratings of that item by the recommending users, ~tep 60. The recon,mendation-fraction is typically a fraction ranqing from zero to one.
The pairing of users to determine the difference in ratings and to generate an agreement scalar is shown in more detail in Fig. ~. For each pair of users, the item is set to first item, step 70, and loop 72 is entered until each of all 1 3 2 4L ~ ~ ~r pos6~ble item h~s been examined. The lte~ ls rec~lled, step 7~, and the ~tem~ for both members of the p~ir are Datched to ee lf that ~tem wa~ sampled by both me~bers, ~tep 76. A numb~r of rating6 for movles are provided ~6 an e~ample in Table Is ~LE Is RA~I~Ç~
Movie ~itle Smit~ Jones Wesso~
Star War~ 8 11 10 ~he Untouchables 10 9 4 Beverly ~ills Cops - 10 10 Fletch 10 - g Caddyshack 7 - 11 qhe rating difference is determined, step 78, and a closeness value is obtained for that difference, step 80. In one construction, the closeness value is obtained from a look-up table such as Table II:
132~7v WLE I I: I~Ti NG ~0 C~LOSE~ SS-VAhyE
jf~4 C~oliene6B V~l o 10 Step 80 provides a weighting step in which large difference~ in ratings are penalized and similarities are rewarded. In other constructions, the unaltered differences themselves are used.
In yet other embodiment~, ratios or item-specific probabilities of the differences may be compared, or agreement by types or categories of items may be utilized.
In this embodiment, the clo~ene6s-value i8 added to ~
running total, step 82, and the count of ite~ sampled by both members is incremented, step 8~. After the last item has been processed, step 86, an aqreemert ~calar is generated, step 88, 132467r for that palr of user6. ~he agreement scalar may be gener~ted by the use of the followlng equations AS - (CV~) ~2n-1)/n2 ~1) where AS i8 the agreement scalar, CVT i6 the closeness-value total, and n i6 the count o items sampled by both users. By the example provided in Tablç~ I and II, Smith and Jone~ have sampled two items in common having a difference in ratings of 3 and 1, respectively, which are assigned closeness values of 4 and 9, respectively. By application of equation (1), the agreement scalar for Smith and Jones is 9.75. Similarly, the closeness-value for the pair of Smith and Wesson is 17 and the agreement scalar is 7.44. The difference in reaction of Smith and Wesson to ~The Untoucbables~ and ~Caddyshack~ led to the smaller agreement scalar between those users. It is evident that the greater the number of items that the users have sampled, the more accurate the agree~ent scalar will be for each of the users with which the selected user i8 paired.
The conversion of the agreement scalar to a weighting value, referred to as a recommendation-fraction, and ad~ustment of the previously establi~hed ratings i6 shown in Fig. 5. One or more recommending users are designated, step 90, from the group of users. ~f the number of users is small, the entire group may be used. Otherwise, a subset of the group, e.g.
sixteen users may be used. Succe~sive ones of the users are 1 32~ ~7~i de6ign~ted as ~elected use~ wh~le the remainder of the ~ub~et are deslqnated as recommending user6.
The recommend~ng u6ers are ranked by order of ~greement scalar, 6tep 92. ~ach recommending user i6 then utilized to adjust the previou61y e tabll~hed predicted ratings for tbe selected user, loop ~ A recommendation-fractlon i8 defined for the agreement Ecalar of the first recommending user, step 94. It i8 desirable to rank the recommending person6 by ascending order of agreement scalar and in that order as6igning to the ranked predicting person~ progres6ively larger weighting values. In one embodiment, for the fourth highest agreement 6calar a recommendation-fraction of 1/16 is defined, for the third highest a recommendation fraction of 1/8 is defined, for the second highest a recommendation-fraction of 1/4 i defined, and for the highest a recommendation-fraction of 1/2 is defined.
All other agreement scalars are assigned a value of zero or their recon,mendation-fraction. The lists of items for the recommending user and the selected user are matched to identify, step 96, items 6a~pled by the recommending user but not by the selected user. Each identifying item is analy2ed in loop 98 in which the difference between ratings is determined, the recommendation-fraction and the difference are combined, and the rating is adjusted by the combination, steps 100, 102, 104, respectively. When the recommendation-fraction has been combined with the difference for each item including the last 1 3 2 4 ~ 7 ) ldentified item, 6tep 106, the next recommending u~er 18 selected, ~tep 108.
In one embodiment, a difference between the ratinqs 18 determined by subtracting the prevlou~ly estlmated rat1ng of the selected user from the actual eating of the recommending user.
The difference i8 then multiplled by the recommendation-fraction to obtain an adjustment, and the adjustment $6 added to the previou61y e6timated rating. When the recommending user6 are ranked in order of lowest to highest agreement scalar, the relative adjustment accorded by the recommending user with the highest scalar i~ enhanced. That is, his weighting effect is not diluted by later adjustments from less appropriate recommending users.
While the terms ~person~ and ~user~ as used above refer to a human being, the terms are used in their broadest sense to refer to any entity which exhibits a subjective but not random reaction to an item. The above-de6cribed sy~tem and method of operation according to the present invention similarly apply to more than movieæ, record albums, computer games, ol other consumer items. For example, reaction6 can be predicted for travel destination~, hotels, restaurants, or career6. Further, predlctions among categories can be accomplished, e.g., recommending books based on the ratings of movies. The system and method according to this invention are particularly useful for items which have significance in and of themselves to people, that is, predicting the reactions of people to the items ., 1324~7r~
benefits the people in optimally d~rectlng their lnve~ment of time and money ln choosing and 6ampling itecs.
Although sp4eific feature6 of the lnvention ale 6hown ln some drawing6 and not other~, thls i6 for convenience only a~
each feature ~ay be combined with any or all of the other feature6 in accoraance with the invention.
Other embodi~ent6 will occur to tho6e skilled in the art and are with$n the following claim6:
What i6 claimed i6:
Claims (25)
1. A method of automatically predicting, for a user selec-ted from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the reaction predictions being based on other items previously sampled by that user, comprising:
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the difference in ratings between that predicting user and the selected user; and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selected user to more closely predict the actual reaction of the user to that item.
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the difference in ratings between that predicting user and the selected user; and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selected user to more closely predict the actual reaction of the user to that item.
2. A method of using a computing device to automatically predict, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the reaction predic-tions being based on other items previously sampled by that user, comprising:
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, the step of defining including entering, into an input device of the computing device, information representing the reaction of the selected user to items sampled by that user;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the difference in ratings between that predicting user and the selected user; and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selected user to more closely predict the actual reaction of the user to that item.
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, the step of defining including entering, into an input device of the computing device, information representing the reaction of the selected user to items sampled by that user;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the difference in ratings between that predicting user and the selected user; and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selected user to more closely predict the actual reaction of the user to that item.
3. The method of claim 2 in which pairing includes successively matching, for each pair, items sampled by both mem-bers and, for each matched item, comparing the ratings of one member from the rating of the other member to obtain the differ-ence in ratings.
4. The method of claim 3 in which pairing further includes converting, for each pair, the difference in ratings for each matched item to a closeness value, and combining the closeness values for the members of that pair.
5. The method of claim 4 in which pairing further includes weighting, for each pair, the combined closeness values by the number of items sampled by both members to generate the agreement scalar.
6. The method of claim 2 in which designating and convert-ing included defining a greater weighting value for predicting users having a larger agreement scalar and defining a lesser weighting value for predicting users having a smaller agreement scalar.
7. The method of claim 2 in which designating and convert-ing includes ranking the predicting users by ascending order of agreement scalar and in that order assigning to the ranked predicting users progressively larger weighting values.
8. The method of claim 2 in which applying includes combin-ing the weighting value for each predicting user with the differ-ence between the actual rating by that predicting user and the previous predicted rating by the selected user for each unsampled item, and summing the combination with the previously predicted rating.
9. The method of claim 2 in which pairing includes successively pairing the selected user with each other user in the group.
10. The method of claim 2 in which pairing includes defining a subset of other users in the group to be successively paired with the selected user.
11. The method of claim 2 further including successively selecting the remainder of the users in the group to adjust the reaction predictions for each user in the group.
12. A method of selectively recommending, for each user succesively selected from a group of users, items sampled by one or more users in the group but not yet sampled by the selected user, the recommendations being based on other items previously sampled by that user and being represented by a scalar rating for each item, the method comprising:
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
generating for each pair an agreement scalar representing the overall rating agreement between the members of that pair;
designating at least one of the other users as recommending users;
converting, for each of the recommending users, the associa-ted agreement scalar into a recommendation-fraction;
identifying items not yet sampled by the selected user;
establishing an initial scalar rating for each identified item for the selected user;
applying the recommendation-fractions to proportionally decrease the difference between one of the initial scalar rating for each identified item and a scalar rating previously establi-shed for each identified item for the selected user and the actual ratings of that item by the recommending users to adjust the recommendations for the selected user;
successively selecting the remainder of the users in the group to adjust the recommendations for each user in the group by the above-recited steps; and presenting, for each user, a plurality of items based on the recommendations for that user.
defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item;
successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to determine the difference in ratings for items sampled by both members of each successive pair;
generating for each pair an agreement scalar representing the overall rating agreement between the members of that pair;
designating at least one of the other users as recommending users;
converting, for each of the recommending users, the associa-ted agreement scalar into a recommendation-fraction;
identifying items not yet sampled by the selected user;
establishing an initial scalar rating for each identified item for the selected user;
applying the recommendation-fractions to proportionally decrease the difference between one of the initial scalar rating for each identified item and a scalar rating previously establi-shed for each identified item for the selected user and the actual ratings of that item by the recommending users to adjust the recommendations for the selected user;
successively selecting the remainder of the users in the group to adjust the recommendations for each user in the group by the above-recited steps; and presenting, for each user, a plurality of items based on the recommendations for that user.
13. The method of claim 12 in which pairing includes successively matching, for each pair, items sampled by both members and, for each matched item, comparing the rating of one member with the rating of the other member to obtain the difference in ratings.
14. The method of claim 13 in which pairing further includes converting, for each pair, the difference in ratings for each matched item to a closeness value, and combining the closeness values for the members of that pair.
15. The method of claim 14 in which pairing further includes weighting, for each pair, the combined closeness values by the number of items sampled by both members to generate the agreement scalar.
16. The method of claim 15 in which designating and convert-ing includes sorting the recommending users by ascending order of agreement scalar and in that order assigning to the ranked predicting users progressively larger weighting values.
17. The method of claim 16 in which applying includes combining the weighting fraction for each recommending user with the difference between the actual rating by that recommending user and the previous predicted rating by the selected user for each identified item, and summing the combination with the previously established rating.
18. The method of claim 17 further including initially set-ting the established rating for each unsampled item for each selected user to a low rating value.
19. A system for predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by the selected user, the predictions being based on other items previously sampled by that user, comprising:
means for defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, said means for defining including input means for entering information representing the reaction of the selected user to items sampled by that user;
means for successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to deter-mine the difference in ratings for items sampled by both members of each successive pair;
means for designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the overall difference in ratings between that predicting user and the selected user; and means for applying the weighting values to items not yet sampled by the selected user to proportionally alter the differ-ence between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selec-ted user to more closely predict the actual reaction of the user to that item.
means for defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, said means for defining including input means for entering information representing the reaction of the selected user to items sampled by that user;
means for successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to deter-mine the difference in ratings for items sampled by both members of each successive pair;
means for designating at least one of the other users as a predicting user and assigning a weighting value to each of the predicting users based on the overall difference in ratings between that predicting user and the selected user; and means for applying the weighting values to items not yet sampled by the selected user to proportionally alter the differ-ence between a rating previously predicted for each item not yet sampled by the selected user and the ratings of that item by the predicting users to adjust the reaction predictions for the selec-ted user to more closely predict the actual reaction of the user to that item.
20. A computing device for predicting, for a user selected from a group of users, the reactions of the selected user to items sampled by one or more users in the group but not sampled by that user, the prediction being based on other items previously sampled by that user, comprising:
means for defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, said means for defining including input means for entering information representing the reaction of the selected user to items sampled by that user;
means for successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to deter-mine the difference in rating for items sampled by both members of each successive pair, said means for pairing including means for generating for each pair an agreement scalar representing the overall rating difference between the members of that pair;
means for designating at least one of the other users as a predicting user and for converting, for each of the predicting users, the agreement scalar into a weighing fraction;
means for establishing an initial scalar rating for each identified item for the selected user; and means for identifying items not yet sampled by the selected user and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between one of the initial scalar rating for each identified item and a rating previously predicted for each identified item and the ratings of that item by the predicting users to adjust the reac-tion predictions for the selected user to more closely predict the actual reaction of the user to that item.
means for defining, for each item sampled by the selected user, a scalar rating representing the reaction of the selected user to that item, said means for defining including input means for entering information representing the reaction of the selected user to items sampled by that user;
means for successively pairing the selected user with other users in the group for whom have been defined scalar ratings for at least some of the items sampled by the selected user to deter-mine the difference in rating for items sampled by both members of each successive pair, said means for pairing including means for generating for each pair an agreement scalar representing the overall rating difference between the members of that pair;
means for designating at least one of the other users as a predicting user and for converting, for each of the predicting users, the agreement scalar into a weighing fraction;
means for establishing an initial scalar rating for each identified item for the selected user; and means for identifying items not yet sampled by the selected user and applying the weighting values to items not yet sampled by the selected user to proportionally alter the difference between one of the initial scalar rating for each identified item and a rating previously predicted for each identified item and the ratings of that item by the predicting users to adjust the reac-tion predictions for the selected user to more closely predict the actual reaction of the user to that item.
21. A method of using a computing device to automatically update, for a user selected from a group of users, estimated scalar ratings for items sampled by one or more users in the group but not yet sampled by the selected user to more closely predict the reaction of the selected user to the not-yet-sampled items, the method comprising:
defining, for each item sampled by the selected user, an actual scalar rating representing the actual reaction of the selected user to that item, the step of defining including enter-ing, into an input device of the computing device, information representing the reaction of the selected user to items sampled by that user;
defining, for each item sampled by other users in the group, an actual scalar rating representing the actual reaction of each user to that item, including entering into the input device information representing the reaction of the other users to items sampled by each user;
successively pairing the selected user with other users in the group for whom have been defined actual scalar ratings for at least some of the items sampled by the selected user to determine the difference in actual ratings for items sampled by both members of each successive pair;
designating a plurality of the other users as updating users and assigning a weighting value to each of the updating users based on the difference in actual ratings between that updating user and the selected user;
establishing an initial estimated rating for items not yet sampled by the selected user; and applying the weighting values, for items not yet sampled by the selected user, to proportionally alter the difference between one of (a) the initial estimated rating for each item not yet sampled and (b) and estimated rating previously updated for each item not yet sampled by the selected user and the actual ratings of that item by the updating users to adjust the estimated ratings for the selected user to more closely predict the actual reaction of the user to that item.
defining, for each item sampled by the selected user, an actual scalar rating representing the actual reaction of the selected user to that item, the step of defining including enter-ing, into an input device of the computing device, information representing the reaction of the selected user to items sampled by that user;
defining, for each item sampled by other users in the group, an actual scalar rating representing the actual reaction of each user to that item, including entering into the input device information representing the reaction of the other users to items sampled by each user;
successively pairing the selected user with other users in the group for whom have been defined actual scalar ratings for at least some of the items sampled by the selected user to determine the difference in actual ratings for items sampled by both members of each successive pair;
designating a plurality of the other users as updating users and assigning a weighting value to each of the updating users based on the difference in actual ratings between that updating user and the selected user;
establishing an initial estimated rating for items not yet sampled by the selected user; and applying the weighting values, for items not yet sampled by the selected user, to proportionally alter the difference between one of (a) the initial estimated rating for each item not yet sampled and (b) and estimated rating previously updated for each item not yet sampled by the selected user and the actual ratings of that item by the updating users to adjust the estimated ratings for the selected user to more closely predict the actual reaction of the user to that item.
22. The method of claim 12 in which presenting includes providing a listing of a limited number of highly recommended items not yet sampled by the selected user.
23. The method of claim 12 in which establishing includes providing a low scalar rating value as the initial scalar rating.
24. The computing device of claim 20 in which said means for establishing establishes said initial scalar rating based on information entered through said input means.
25. The computing device of claim 20 in which said means for establishing includes memory for storing said initial scalar rating.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US07/103,848 US4870579A (en) | 1987-10-01 | 1987-10-01 | System and method of predicting subjective reactions |
| CA000609923A CA1324675C (en) | 1987-10-01 | 1989-08-30 | System and method of predicting subjective reactions |
| US07/411,857 US4996642A (en) | 1987-10-01 | 1989-09-25 | System and method for recommending items |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US07/103,848 US4870579A (en) | 1987-10-01 | 1987-10-01 | System and method of predicting subjective reactions |
| CA000609923A CA1324675C (en) | 1987-10-01 | 1989-08-30 | System and method of predicting subjective reactions |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA1324675C true CA1324675C (en) | 1993-11-23 |
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ID=25672990
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA000609923A Expired - Lifetime CA1324675C (en) | 1987-10-01 | 1989-08-30 | System and method of predicting subjective reactions |
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| Country | Link |
|---|---|
| US (1) | US4870579A (en) |
| CA (1) | CA1324675C (en) |
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| FR2226078A5 (en) * | 1973-04-13 | 1974-11-08 | Sodema | |
| US4041617A (en) * | 1976-07-26 | 1977-08-16 | James Fisher Hollander | Apparatus and method for indication and measurement of simulated emotional levels |
| US4205464A (en) * | 1977-09-15 | 1980-06-03 | Baggott Patrick D | Apparatus and method for determining the extent of mutuality between partners |
| US4348740A (en) * | 1978-04-04 | 1982-09-07 | White Edward A | Method and portable apparatus for comparison of stored sets of data |
| FR2461302A1 (en) * | 1979-07-13 | 1981-01-30 | Lassays Yves | Calculating machine to analyse personality and predict future(s) - personal data is entered and predictions made by internal program using principles of 'numerology' |
| US4646145A (en) * | 1980-04-07 | 1987-02-24 | R. D. Percy & Company | Television viewer reaction determining systems |
| US4331973A (en) * | 1980-10-21 | 1982-05-25 | Iri, Inc. | Panelist response scanning system |
| US4566030A (en) * | 1983-06-09 | 1986-01-21 | Ctba Associates | Television viewer data collection system |
| US4658290A (en) * | 1983-12-08 | 1987-04-14 | Ctba Associates | Television and market research data collection system and method |
| US4602279A (en) * | 1984-03-21 | 1986-07-22 | Actv, Inc. | Method for providing targeted profile interactive CATV displays |
| US4630108A (en) * | 1984-03-26 | 1986-12-16 | A. C. Nielsen Company | Preprogrammed over-the-air marketing research system |
| DE3529301A1 (en) * | 1984-08-28 | 1986-03-27 | Jost von Dr. Grüningen Fellenberg | Psychotechnical method and device for conducting the method |
| JP2520588B2 (en) * | 1985-06-11 | 1996-07-31 | 橋本コーポレイション 株式会社 | Individual TV program guide creation device |
| US4647964A (en) * | 1985-10-24 | 1987-03-03 | Weinblatt Lee S | Technique for testing television commercials |
| US4682956A (en) * | 1985-11-27 | 1987-07-28 | Leonard Krane | Apparatus and method for learning about the relationships and personalities of a group of two or more persons |
| US4781596A (en) * | 1986-06-16 | 1988-11-01 | Weinblatt Lee S | Survey technique for readership of publications |
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1989
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| US4870579A (en) | 1989-09-26 |
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