CN1423782A - 确定网络上交互有效性的系统和方法 - Google Patents

确定网络上交互有效性的系统和方法 Download PDF

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CN1423782A
CN1423782A CN01808051A CN01808051A CN1423782A CN 1423782 A CN1423782 A CN 1423782A CN 01808051 A CN01808051 A CN 01808051A CN 01808051 A CN01808051 A CN 01808051A CN 1423782 A CN1423782 A CN 1423782A
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CN1262949C (zh
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S·多利奥夫
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Fly Upward Management Co Ltd
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Abstract

本发明的方法和系统利用有限数据来获得与一个环球网站点的任何指定的交互的有效性有关的信息。一旦有效信息是可用的,经营者确定是否继续为指定用户提供交互。经营者收集数据(200),将数据作为值插入到确定的数学方程中(204),存取用于每一个交互的风险值(206)。基于不受欢迎的交互,该确定与是否报告环球网站点交互统计数据有关,如何处理对这类不受欢迎的交互的帐单和付费以及向正在以一种不受欢迎的方式与该环球网站点交互的用户发送何种内容。

Description

确定网络上交互有效性的系统和方法
相关申请的交叉参考
本申请要求2000.2.14申请的U.S.临时申请号No.60/182,142的优先权，在此合并作为参考。
发明领域
本发明涉及统计建模，更准确地说，涉及使用统计建模来确定计算机网络上交互的有效性。
背景
计算机网络上信息的传送对机关、公司以及个人通信以及管理企业来说已经变成越来越重要的一种方法。这些年来计算机网络已经从独立和单独实体建立的以满足唯一群体的需要的这些年已经成长为内连完全不同的物理网络并允许它们作为同等的系统起作用的巨大的因特网。目前，现有的最大计算机网络是因特网。因特网是使用共同的协议通信的全世界互连的计算机网络。数百万的计算机，从低档个人计算机到高档巨型计算机均连接到因特网。
许多网络经营者，诸如因特网上的商业站点的经营者有理由来确定与该网络或环球网站点交互的指定用户的有效性。例如，随着近来因特网上实施交易的商业企业的增长，环球网站点的经营者可能想确定用户与环球网站点交互是否无效或甚至是犯罪的欺诈。必须对与环球网站点交互的有效性感兴趣的环球网站点经营者的四个例子是提供有奖比赛(trivia contest)的环球网站点经营者，该环球网站点经营者试图确定唯一用户而不要求来自其用户的详细的识别信息、搜索引擎经营者、以及对投放收费的环球网站点经营者。
第一，提供有奖比赛的环球网站点经营者可能面对隐藏他们的真实身份并多次进入比赛的参与者，在该比赛中，奖金被授予正确回答一组问题的参与者。环球网站点经营者冒有滥用该处理而被授予奖金的环球网站点用户的风险并因此冒财政和信用两方面的风险。如果环球网站点经营者希望环球网站点的相对匿名使用并保持公平比赛，经营者必须找出评估在有奖比赛中哪些进入是合法竞争者的进入以及哪些进入是试图获取不公开利益的竞争者的重复进入的一些方法。
第二，许多环球网站点经营者试图计算访问一站点的唯一用户以确定对在该站点上投放广告的价格。尽管用户曲奇(user cookies)的用户是共同的，由大多数浏览者以及环球网服务器提供的曲奇机制能轻易地被甚至纯洁的环球网站点用户阻挠。试图基于唯一访问者的数量对广告进行收费的环球网站点经营者在报告唯一访问者的数量时有尽可能真实的责任，并且非常适合不基于曲奇机制来确认或无效由用户曲奇确定的唯一用户的总数。不能满足这些将导致损失广告收入。
第三，搜索引擎提供者可能对确定和执行由搜索引擎用户请求的某些搜索术语的次数感兴趣。如果搜索引擎经营者提供相对匿名访问，对有人连续查询相同搜索术语而书写一脚本来说非常简单。如果搜索引擎经营者不可能识别来自真实用户查询的这类非法查询，搜索引擎经营者会错报对该搜索引擎出现的查询用户(与软件robots或bot相反)的真实数量。这种搜索术语频率的错报破坏该搜索引擎的整体信用，同时该搜索引擎经营者面临将一特殊搜索术语按通用报告的风险，当实际上它恰好是一个用户的操作的时候。
第四，按投放和业绩付费，环球网站点经营者在环球网站点上出租空间给广告商，仅当广告商出租的空间由环球网站点用户使用诸如对该广告商站点建立一链接时才对他们进行收费。按投放和业绩付费，环球网站点经营者需要能保证对在经营者站点上发生的业绩交互的付费的有效性。当一恶意用户对广告商的产品或服务并不感兴趣时，该用户能轻易在该经营者站点上产生导致对广告商(按投放和业绩空间付线的买方)收费的交互。
上述所有例子说明了几种情况，其中能从与一环球网站点的无效交互确定有效对环球网站点经营者来说相当重要。对本领域的技术人员来说将认可唯一性和可靠性的已知度量如用户曲奇、客户IP地址或用户代理识别均是容被破坏的机制。因此，需要能克服上述缺陷的一个系统来验证环球网站点交互。
优选实施例的概述
本发明寻求通过提供用于收集和处理用户与一网络的交互的数据的一系统和方法来解决上述问题。更准确地说，该系统和方法将统计建模技术应用于创建对指定的感兴趣的网络交互风险值。经营者使用这些风险值来确定与该网络的交互的合法性或有效性。为便于解释，使用一个例子，其中一经营者想要确定一种类型的网络如因特网上交互的有效性。
在一个实施例中，为确定交互的有效性，经营者收集有关该交互的数据。所收集的数据包括行为的聚合度量(aggregate measure)以及指定交互的唯一特征数据。行为的聚合度量包括如对指定时限每个因特网协议(IP)地址的若干点击、每个用户会话的若干唯一查询、对指定时限的若干网络点击以及对指定时限的不同介绍伙伴(distinctreferral partner)产生的若干点击。指定交互的特征包括如交互的次数、交互的搜索术语以及交互的IP地址。因此，经营者使用预测模型和数据来识别与该环球网站点的交互的有效性。如果无效的交互被识别，经营者删除该无效交互，如在对基于该交互的客户收费前。
附图的简单说明
图1根据优选实施例，描述一个示范性计算机网络系统。
图2是根据优选实施例，说明风险值的分配的流程图。
图3是根据优选实施例，说明收集的数据的聚合度量的块图。
图4是根据优选实施例，说明收集的唯一特征数据的块图。
图5是根据优选实施例，说明用于评估交互的风险值的方法的块图。
图6是根据优选实施例，说明用于将随机类数据方程应用到网络交互的方法的块图。
图7是根据优选实施例，说明用于验证风险评估方程的方法的块图。
图8是根据优选实施例，说明使用追溯分析估计一风险模型的方法的块图。
优选实施例的详细说明
公开了用于确定基于计算机网络系统客户/服务器的用户交互的有效性的方法和系统。为便于说明，阐述特殊术语以提供对本发明的全面理解。特殊的方程式，包括方程式的形式和常数，通过反复试验得出并且这些方程对发明人来说在提交申请时是已知的最好的方程。提供的特定应用的说明仅作为例子。对本领域的技术人员来说对优选实施例的各种修改是显而易见的，并且在此定义的一般原理也适用于不脱离本发明的精神和范围的其他实施例和应用。
现有参考附图，图1是用在本发明的优选实施例中的构造为客户/服务器结构的分布式系统10的例子。“客户”是使用与其无关的另一类或组的服务的一个类或组的成员。在计算机网络如因特网的环境中，客户是请求由另一进程如公知的服务器程序提供的一个服务的进程(即一般所说的程序或任务)。客户进程使用所请求的服务而不需要知道有关另一服务器程序或服务器本身的任何工作细节。
“服务器”通常是可通过通信媒介如因特网访问的一远程计算机系统。客户进程可在第二计算机系统中被启动，并在允许多个客户利用服务器收集信息的能力的通信媒介上与服务器进程进行通信。因此，服务器实质上充当计算机网络的信息提供者。
图1的块图表示包括均连接到网络20上的多个客户计算机12和多个服务器14的分布式系统10。在下文中网络20通常指因特网。尽管本发明的系统和方法对因特网来说非常有用，应当理解客户计算机12和服务器14可通过若干不同类型的网络中的一个连接在一起。这种网络可包括局域网(LANs)，其他广域网(WANs)以及在电话线上访问的区域网如商业信息服务。客户和服务器进程甚至可包括在唯一计算机上同步执行的不同程序。
客户计算机12可是常规的个人计算机(PCs)、工作站或任何其他大小的计算机系统。每个客户计算机12通常包括一个或多个处理器、存储器、输入/输出设备以及一网络接口如常规的调制解调器。服务器14可是相同配置。然而，服务器14每一个可包括通过唯一专用网络连接的许多计算机。网络20可包括几十万唯一的计算机网络。
本系统和方法的优选实施例包括两个主要部分：网络20上的一个或多个服务器14以执行数据处理、以及控制计算机的软件。在此描述用于网络硬件和软件的优选实施例，然而，也可使用其他等效的硬件和软件。
经营者使用一个或多个服务器14来确定环球网站点上交互的有效性。为确定环球网站点上交互的有效性，经营者按如下所述的方式收集数据。使用如下描述的算法来处理收集的数据。处理所收集的数据的优选方法在John Joseph Carrasco et al.所拥有的专利申请序列号No.09/502,692，2000年2月11日中请的，发明名称“SYSTEM ANDMETHOD FOR RAPID COMPLETION OF DATA PROCESSING TASKS DISTRIBUTEDON A NETWORK”描述过，在此合并作为参考。
图2中描述的流程图表示风险值的分配以确定交互的有效性。经营者收集指定交互行为的聚合度量以及唯一特征数据(块200)。参考图3，聚合度量的收集可包括如收集有关对指定时限的每个因特网协议(IP)地址的若干点击(块300)、每个用户会话的唯一查询的数量(块302)以及对指定时限网络点击的数量(块304)。另外的聚合度量也可被使用如对指定时限具有与经营者站点HTML超级链接的不同介绍伙伴产生的点击的数量(块306)，或使用较少的聚合度量。在块308，其他聚合度量也可被收集，如表1中所示：
表1.
A.查询列表结果聚合度量—用来帮助识别接收滥用点击的特定的搜索结果：
1.单位时间每个搜索列表点击的唯一用户Id’s的数量；
2.单位时间每个搜索列表点击的唯一用户会话的数量；
3.单位时间每个搜索列表点击的唯一用户输入格式化查询的数量；
4.单位时间每个搜索列表点击的唯一搜索标识符的数量；
5.单位时间每个搜索列表点击的唯一IP地址的数量；
6.单位时间每个搜索列表点击的唯一C网络的数量；
7.单位时间每个搜索列表点击的唯一引导网站的数量；以及
8.单位时间每个搜索列表点击的点击总数。
B.引用源(referring source)聚合度量--用来帮助识别引入会话、搜索或点击的不表示使用搜索服务的实际用户的引用源：
1.单位时间每个引用源的唯一用户Id’s的数量；
2.单位时间每个引用源的唯一用户会话的数量；
3.单位时间每个引用源的唯一用户输入格式化查询的数量；
4.单位时间每个引用源的唯一数据库输入格式化查询的数量；
5.单位时间每个引用源的唯一搜索标识符的数量；
6.单位时间每个引用源的唯一IP地址的数量；
7.单位时间每个引用源的唯一C网络的数量；
8.单位时间每个引用源的付费点击总数；
9.单位时间每个引用源的不付费点击总数；
10.单位时间每个引用源的唯一搜索列表点击的数量；
11.单位时间每个引用源所有点击总数。
C.广告商聚合度量--用来帮助识别攻击特定广告商的攻击
1.单位时间每个广告商接收的任何一次性点击的唯一用户Id’s的数量；
2.单位时间每个广告商接收的任何一次性点击的唯一用户会话的数量；
3.单位时间每个广告商接收的任何一次性点击的唯一用户输入格式化查询的数量；
4.单位时间每个广告商接收的任何一次性点击的唯一数据库输入格式化查询的数量
5.单位时间每个广告商接收的任何一次性点击的唯一搜索标识符的数量；
6.单位时间每个广告商接收的任何一次性点击的唯一IP地址的数量；
7.单位时间每个广告商接收的任何一次性点击的唯一C网络的数量；
8.单位时间每个广告商接收的任何一次性点击的一次性点击总数；
9.单位时间每个广告商接收的任何一次性点击的唯一搜索列表点击的数量；
10.单位时间每个广告商接收的任何一次性点击所有点击总数；以及
11、单位时间每个广告商接收的任何一次性点击的点击间的平均时间。
D.IP地址聚合度量--用来帮助识别滥用的点击IP地址：
1.单位时间每个IP地址产生的任何一次性点击的唯一用户Id’s的数量；
2.单位时间每个IP地址产生的任何一次性点击的唯一用户会话的数量；
3.单位时间每个IP地址产生的任何一次性点击的唯一用户输入格式化查询的数量；
4.单位时间每个IP地址产生的任何一次性点击的唯一数据库输入格式化查询的数量
5.单位时间每个IP地址产生的任何一次性点击的唯一搜索标识符的数量；
6.单位时间每个IP地址产生的任何一次性点击的一次性点击的总数；
7.单位时间每个IP地址产生的任何一次性点击的唯一搜索列表点击数量；
8.单位时间每个IP地址产生的任何一次性点击的所有点击总数；
9.单位时间每个IP地址产生的任何一次性点击的的点击间的平均时间；
10.单位时间每个IP地址产生的任何一次性点击产生的总收入。
E.C网络聚合度量--用来帮助识别滥用点击网络的IP地址：
1.单位时间每个C网络产生的任何一次性点击的唯一用户Id’s的数量；
2.单位时间每个C网络产生的任何一次性点击的唯一用户会话的数量；
3.单位时间每个C网络产生的任何一次性点击的唯一用户输入格式化查询的数量；
4.单位时间每个C网络产生的任何一次性点击的唯一数据库输入格式化查询的数量
5.单位时间每个C网络产生的任何一次性点击的唯一搜索标识符的数量；
6.单位时间每个C网络产生的任何一次性点击的唯一IP地址的数量；
7.单位时间每个C网络产生的任何一次性点击的一次性点击总数；
8.单位时间每个C网络产生的任何一次性点击的唯一搜索列表点击的数量；
9.单位时间每个C网络产生的任何一次性点击的所有点击总数；
10.单位时间每个C网络产生的任何一次性点击点击间平均时间；以及
11、单位时间每个C网络产生的任何一次性点击的产生的总收入。
参考图4，经营者也为指定的交互收集唯一特征数据，如关于用户的起始地址(块400)、交互的时间(块402)、交互的类型(块404)以及假定的用户唯一性的度量如当有效时的用户曲奇信息(块406)。曲奇是允许环球网站点服务器为特定用户在用户自己的计算机上存储来自服务器的信息的机制。在块408，其他唯一特征数据也能被收集，如表2所示：
表2.
1.产生收入点击的日期；
2.产生收入点击的时间印迹；
3.产生收入点击的会话标识符；
4.产生收入点击的的引用源；
5.产生收入点击的IP地址；
6.产生收入点击的C网络；
7.先于产生收入点击的前用户输入格式化查询；
8.先于产生收入点击的前数据库输入格式化查询；
9.产生收入点击的的查询列表标识符；
10.产生收入点击的的查询标识符；
11.每次产生收入点击的所产生的收入总额；
12.搜索列表位置(按结果分页)；
13.执行每个搜索返回的产生收入产生搜索列表；
14.执行每个搜索返回的总的搜索列表；以及
15.与环球网站点交互的用户代理。
使用聚合度量和唯一特征数据，优选实施例系统和方法基于统计模型提供有关有效和无效、合法与非法、以及受欢迎和不受欢迎的环球网站点交互的信息。
现在再参考图2，当使用模型技术时，经营者收集数据点直到收集到足够的交互以提供统计的有效结果(块202)。所要求以生成统计结果的交互的数量改变以及由所收集的数据和兴趣的统计推理而定。例如，对便于每天上千万或更多交互的环球网站点来说，可收集2000个随机选择的交互以确定与站点的交互所花费的平均假定持续时间。在经营者的环球网站点的环境中在标记为“dog”的链接上的连续点击被统计地区分为合法或合法前可收集约20,000个数据点。本领域的技术人员能理解统计员能提供对统计推理的任何指定问题所要求的最小数据点量的计算。
另外，本领域的技术人员出能理解每天便于上千万交互的环球网站点能在相对短的时间范围内为许多统计问题提供所要求的采样数据点量。例如，一天便于43.2百万交互的环球网站点每秒对大约500个交互进行分类。对经营者感兴趣的问题所要求的数据点的上限是300.000，然后，这种大容量环球网站点能每隔十分钟获得所要求的数据。
为确定用户与环球网站点的交互的有效性，收集聚合和唯一特征数据，作为值插入到以下描述和处理的数学方程式中(块204)。所收集的总的和唯一特征数据可包括有关搜索、IP地址，搜索时间、搜索会话或依靠搜索引擎的一个视在用户的搜索组、点击时间、接收点击的广告商以及广告商乐于对接收的一次点击支付的价钱的信息。然后聚合和唯一特征数据可被简化为搜索的详细资料，如搜索术语：dog，时间：12:00:00PM，IP地址：192.168.1.1，广告商ID：ABC，会话ID：XYZ，支付：$0.25。该数据被包括在累计中，如，对指定时间间隔由IP地址192.168.1.1的点击数以及对指定时间间隔有关词“dog”的搜索次数。
然后，聚合度量和唯一特征数据被处理以评估对每个交互的风险值(块206)。参考图5，聚合度量数据和唯一特征数据被合并(块500)。以列表形式，指定数据流部分如下所示：
表3
唯一特征 聚合度量
时间印迹 IP地址 搜索术语 点击/IP/小时 搜索/术语/小时
12:00: 192.168. Dog  2  2
  00   1.1
  12:35:00   192.168.1.1   Cat   2   1
  12:59:59   192.168.5.5   Dog   1   2
用数学方程式的形式表示的模型适用于对该环球网站点感兴趣的每个交互，或交互组。数学模型的准确表示可随时间改变，但该方程式可分为两个类。
经营者采用一种方法，一种概率方法(块502)。概率方法方程式被表示以便该结果可迅速被转换成一种概率。使用商业上可用的统计软件如SASTM系统、将该商业上可用软件的调用应用到逻辑回归例程可推导出该方程式。典型的方程式可在由Hosmer & Lemeshow编写的，由Wilery在1989年、SAS/STAT User’s Guide，Vol.2，Version6，4thEdition copyright 1990，SAS Institute，Cary，North Carolina，USA出版，名为“Applied Logistic Regression”，在此合并作为参考。通常该方程式为y＝x’+k，其中x’为有系数的参数的矢量；k是常数以及y是结果。在逻辑斯谛回归模型的情况中，该结果通过下述公式
被转换成概率：概率＝e(result)/(1+e(result))。常数e是由无穷级数 Σ n = 0 n = b 1 / n !
来描述，约为2.71828。该概率值是0到1间的一个数值。
由概率类表示的概率是属于两个类中的一个的变量或变量组的概率，例如，与一个站点的有效或无效交互。描述与一个环球网站点的合法和非法交互的典型的概率方程如下：
(方程1)结果＝3.03+1.45*一时内对一次点击的唯一链接对方+14.77*小时2内广告商收入-6.38*(一小时内唯一用户曲奇/一小时内点击数)。
(方程2)结果＝4.786+3.85*(#唯一搜索查询/#一小时内广告商站点的点击+0.467(#对广告商链接付费的企业伙伴)/#一小时内观测到的唯一用户对话)+3如果星期二的8pm和午夜间发生相互作用。
方程3-5在形式上与1和2相同但它们用Perl编程语言来表示：
(方程3)
my$resulthog＝($sbc2click*5.2378)-($uid2clik*9.5020)-($rev2rawq*0.0275)+($raw2clik*2.3453)+7.3735；
my$probhog＝(exp$resulthog)/(1+exp$resulthog)。
这两行Perl代码用来识别滥用付费列表的IP地址
(方程4)
my$resultquery＝($sbc2clik*5.7424)+($src2sess*5.3571)-($src2clik*6.7800)-($flag*0.3509)-($logent*1.7096)+1.0651；
my$probquery＝(exp$resultquery)/(1+exp$resultquery).
这两行Perl代码用来识别基于一查询和激励收费点击的成员的过分点击。
(Equation 5)my$resulthotspot＝(($a[$ipct-1])*0.0136)-(($a[$sumrev-1])*0.00006)+(($a[$avgtimeDiff-1])*0.00145)+(($a[$avgtimeDiff1])*($a[$ipct-1]-1)*2.3453)+3.1522；
my$probhotspot＝(exp$resulthotspot)/(1+exp$resulthotspot).
这两行Perl代码用来识别通过分级(不管顺序)的点击和通过高亮搜索术语的点击。
其中sbc2clik＝一小时内在付费列表上产生一次点击的C网的数量；
uid2clik＝一小时内在付费列表上点击的用户Ids的数量；
rev2rawq＝对一付费列表一小时内产生的收入总额/产生该付费列表的原搜索查询的数量；
raw2click＝一小时内产生付费列表的原查询的数量/一小时内在那个付费列表上点击的数量；
src2sess＝一小时内对一付费列表分类的会话Ids与付费源的比率；
src2clik＝一小时内对一付费列表的付费点击与付费源的比率；
Iflag＝一个变量，当src2rawq＞3且src2raw2＜＝5，则为1否则该变量为0；
Logcent＝以10为底的一小时内由付费列表产生的分值的对数；
$a[$Ipct-1]包含一小时内在付费列表上点击的唯一IP地址的数量；
$a[$sumrevt-1]包含一小时内对付费列表以美元为单位的收入总额；以及
$a[$avgtimeDiff-1]包含一小时内在指定付费列表上点击间以秒为单位的平均时间差。
本领域的技术人员将认可方程中的变量和输入变量的系数将随站点上行为模式变化。
然而由于经营者对短期内评估交互有效性的概率感兴趣，方程的形式并不改变。例如，如果在一小时内观测到关于特定广告商的若干不受欢迎的一次性交互，经营商通过提供一警告给广告商该行为已经被识别来响应。然后，广告商可能希望改变他们的广告合同的条件或广告商具有对他们的帐户自动调整以删除不受欢迎的交互的费用。其他措施包括来自提供无效交互的特定IP地址的点击以及关闭广告商的帐户。
如果由概率方法产生的数学模型不适当，或者如果有效性的原状态不具有最大的可信度，经营者可用另一种方法选择创建数学模型。一种方法推导出最小化假定的或估计的组装置的距离的数学形式。该方法在统计学中被称为判别分析并能松散地分类为随机法(块504)。随机法产生被描述为线性判别函数的数学方程组。本领域的技术人员将认可使用商业上可用的统计软件可推导出这些方程。
参考图6，经营者为这些方程赋予一个类，如可接受但不寻常的行为、正常行为和不可接受行为(块600)。计算用于这三个方程的值(块602)。具有最小值的方程是投放特定交互的类(块604)。例如：
(方程6)可接受但不寻常类(ABUC)值＝3*IP地址-7*(用户曲奇/广告商链接上的点击)。
(方程7)正常行为类(NBC)值＝12*付费点击的数量-6*唯一用户查询。
(方程8)不可接受类(UC)值＝(#付费广告商点击)*(唯一IP地址产生的点击)+1/(#唯一搜索查询)。
如果对一个交互，ABUC的值为12，则NBC的值为11.5以及UC的值为13，然后，交互落入UC组中。
再参考图2，经营者最好存储风险值并采取跟踪单个交互的上述分类的适当行动(块208)。所存储的值可用在许多方面中，如产生报告。
再参考图5，优选实施例使用概率和/或随机法来确定与一个环球网站点的单个交互的有效性(块506)。经营者能确定在指定时间这两种方法或方法的组合是否适合于交互。因此，使用所选择的聚合度量和唯一特征数据以及上面描述的方程，经营者能确定用户与该网络交互的有效性。在处理总的和唯一特征数据后，经营者能确定一个特定交互是否属于可接受但不寻常类、正常行为类或不可接受类。经营者按需要处理一个交互，如通过从用来向网络经营者付费的一个报告中删除不可接受类交互。
参考图7，由于更多来自环球网站点的数据被收集并存储，经营者最好定期重新评估用来产生分类判定的数学方程以确保方程的有效性(块700)。商品统计分析软件为输入组产生模型，不管输入的不规则性。因此，一旦经营者使用统计软件产生模型，经营者相对于另一标准评价这些模型(块702)。该另外的标准通常被用于分析时分类结果的度量。例如，如果为种类分类的目的，某人开发软件来执行有关骨头碎片的模式识别分析，该模式识别软件即统计模型最好相对于专攻仅通过查看骨头碎片来识别种类的专家的判断被评价。
观测希望或不受欢迎的许多环球网站点交互的经营者可使用两组模型验证处理。这两个模型验证技术中的第一个是追溯(块706)
参考图8，模型的输出由如采用模型输入数据流的领域专家来评价。领域专家评价多个交互，如上百个或上千个交互，在所建立的模型的基础上，将每一个分类为有效或无效(块800)。然后领域专家能创建二对二的分类表，如下所述(块802)：
表4
领域专家：无效 领域专家：有效
计算机：无效 “真阳性”   “假阳性”
计算机：有效 “假阴性”   “真阴性”
使用该表，领域专家能辨别出有用的模型组，例如，具有极少假阳性和极少假阴性的一个(块804)。可接受的假阳性和假阴性的准确数或百分比由提交给该领域专家的，总的可信度水平和哪种类型的错误最能接受—假阳性或假阴性来决定。在上面的例子中，网络经营者监视用户与一环球网站点的交互来向顾客收费，假阳性相对来说可接受，因为假阳性导致少报合法站点使用率统计，即选择谨慎的做法。假阴性不能接受，因为假阴性导致报告有关具有无效交互的数据。
如在此所述，被认为有效的统计模型具有大约0.25％的假阴性百分比(从0.16％到0.60％范围)以及在2.50％-7.50％的范围内的假阳性百分比。本领域的技术人员将认可可接受的错误分类率随企业相对于做决定的有效时间对可信度和准确度的需要改变。在一些领域，例如，癌症诊断，通常非常低或不存在错分类率被认为是可接受的。然而，在其他领域，较高的错分类率是可接受的。在商业领域中，通常可接受的错分类率如上所述如具有低的假阴性率和oderate假阳性率。同样，本领域的普通技术人员将认可可接受的错分类率是方法和有资格的领域专家的函数。
再参考图7，第二验证技术与第一种相似。区别在于计算机还未将其结合到其模型组合算法中的环球网站点交互数据被插入到模型中并提供给经营者。二对二的分类表如与表4相同被创建但该表表示按时间发生的数据有效性而不是仅应用于模型构造时可用于计算机的数据(块706)。
尽管本发明参考特定说明的实施例已经描述和说明，但本发明并不局限于那些说明性的实施例。本领域的技术人员将认可可做出变形和改变而不脱离由以下的权利要求限定的本发明的范围和精神。因此，落在附加的权利要求及其等效的范围内的这类变形和改变均包含在本发明中。

Claims (43)

1、一种识别在网络上一假定的用户的非法交互的方法，该方法包括：
收集来自网络上用户交互的数据，该数据包括有关该假定用户的聚合度量数据和唯一特征数据；
在数据库中存储该数据；
用聚合度量和唯一特征数据构造预测模型以识别与该网络的非法交互；以及
使用该预测模型识别数据库中的非法交互。
2、如权利要求1所述的方法，进一步包括：
应用由至少一个概率法导出的交互的数学模型；
应用基于至少一个随机法的交互的数学模型；以及
使用随机和概率数学方程确定交互的合法性。
3、如权利要求2所述的方法，其中至少一个概率法包括逻辑斯谛回归。
4、如权利要求2所述的方法，其中至少一个随机法包括线性判别分析。
5、如权利要求2所述的方法，进一步包括：
将一分类赋予由交互的概率和随机导出的数学模型创建的值；
计算交互的概率和随机导出的数学模型的值；
对概率数学模型，将一个交互放入到一类中，基于属于该类的计算的概率；以及
对随机数学模型，将一个交互放入具有最高计算值的类中。
6、如权利要求5所述的方法，进一步包括：
相对于一个标准，定期重新评估概率和随机数学模型；
应用追溯验证处理；以及
应用一预定验证处理。
7、如权利要求1所述的方法，进一步包括：
评估多个网络交互；
创建一分类表；以及
从该分类表确定合法和非法交互模型。
8、如权利要求1所述的方法，进一步包括：
创建非法交互数据库。
9、如权利要求8所述的方法，进一步包括：
评估该数据库以确保交互的概率和随机导出的数学模型是可靠的；以及
舍弃不可靠的交互的概率和随机导出的数学模型。
10、如权利要求9所述的方法，进一步包括：
收集附加的聚合度量和唯一特征数据；以及
基于现有收集的数据以及附加收集的聚合度量和唯一特征数据推导出新的交互的概率和随机导出的数学模型。
11、如权利要求1所述的方法，其中收集聚合度量数据包括：
对指定时限收集有关每个Internet协议地址的若干点击的数据；
收集有关每个用户会话的若干唯一查询的数据；
对指定时限收集有关若干网络点击的数据；
收集有关访问该网络的若干不同介绍伙伴的数据。
12、如权利要求1所述的方法，其中唯一特征数据的收集包括：
收集有关该假定用户的起始地址的数据；
收集有关交互的时间的数据；
收集有关交互的类型的数据；以及
收集有关该假定用户的唯一性的假定度量的数据。
13、用于根据确定环球网站点交互的有效性后的环球网站点交互创建报告的方法，该方法包括：
收集有关该环球网站点交互的聚合度量和唯一特征数据；
用聚合度量和唯一特征数据构造预测模型以识别与该环球网站点不受欢迎的交互；以及
一旦被识别，从报告中删除不受欢迎的交互。
14、如权利要求13所述的方法，进一步包括：
应用统计数据分析技术以在从报告中删除不受欢迎的交互前识别与该环球网站点不受欢迎的交互。
15、如权利要求14所述的方法，其中统计数据分析被用来创建不受欢迎的交互的预测模型。
16、如权利要求15所述的方法，其中预测模型被用来生成概率值和行为组中的至少一个。
17、如权利要求16所述的方法，进一步包括：
公式化有关其概率值或行为组组成与该环球网站点不受欢迎的交互的商业规则。
18、如权利要求15所述的方法，进一步包括：
创建不受欢迎的交互数据库。
19、如权利要求18所述的方法，进一步包括：
评估该数据库以确保预测模型是可靠的；以及
废除不可靠的预测模型。
20、如权利要求19所述的方法，进一步包括：
收集附加聚合度量以及唯一特征数据；以及
基于现有收集的数据和附加收集的数据推导出新的预测模型。
21、如权利要求13所述的方法，其中该预测模型偏向于对用户有利。
22、一种用于根据确定环球网站点交互的有效性后的环球网站点交互创建报告的系统，该方法包括：
第一处理器，收集有关环球网站点用户的聚合度量数据和唯一特征数据；
第二处理器，用聚合度量和唯一特征数据构造预测模型以识别与该环球网站点的不受欢迎的交互，其中一旦被识别，不受欢迎的交互被从报告中删除。
23、如权利要求22所述的系统，其中统计数据分析技术被用来在从报表中删除不受欢迎的交互前识别与该环球网站点不受欢迎的交互。
24、如权利要求23所述的系统，其中统计数据分析被用来创建不受欢迎的交互的预测模型。
25、如权利要求24所述的系统，其中该预测模型被用来产生概率值和行为组中的至少一个。
26、如权利要求25所述的系统，其中公式化有关其概率值或行为组组成与该环球网站点不受欢迎的交互的商业规则。
27、如权利要求24所述的系统，其中创建不受欢迎的交互数据库。
28、如权利要求27所述的系统，其中评估该数据库以确保预测模型是可靠的；以及
废除不可靠的预测模型。
29、如权利要求28所述的系统，其中收集新数据并且基于现有收集的数据和新收集的数据推导出新的预测模型。
30、如权利要求21所述的系统，其中该预测模型偏向于对用户有利。
31、一种评定网络上用户交互的方法，该方法包括：
收集来自网络上用户交互的数据，该数据包括有关用户的聚合度量数据和唯一特征数据；
在数据库中存储该数据；以及
用聚合度量和唯一特征数据构造预测模型以评定与该网络的交互。
32、如权利要求31所述的方法，进一步包括识别网络上非法交互。
33、如权利要求31所述的方法，进一步包括识别网络上无效交互。
34、如权利要求31所述的方法，进一步包括识别网络上未授权的交互。
35、如权利要求31所述的方法，进一步包括：
将概率法应用到交互的数学模型中；
应用随机法以导出交互的数学模型；以及
使用交互的概率和随机数学模型识别网络上交互。
36、如权利要求35所述的方法，进一步包括：
将一类赋予由概率和随机导出的数学方程计算的值；
计算交互的概率和随机数学模型的值；
对概率数学模型，将一交互放入一类中，基于属于该类的计算概率；以及
对随机模型，将一交互放入具有最高计算值的类中。
37、如权利要求36所述的方法，进一步包括：
相对于一个标准，定期重新评估概率和随机数学模型；
应用追溯验证处理；以及
应用预定验证处理。
38、如权利要求37所述的方法，进一步包括：
评价多个网络交互；
创建分类表；以及
由分类表确定交互的模型。
39、如权利要求35所述的方法，进一步包括：
创建交互数据库。
40、如权利要求39所述的方法，进一步包括：
评价该数据库以确保交互的概率和随机数学模型是可靠的；以及
废除不可靠的交互的概率和随机数学模型。
41、如权利要求40所述的方法，进一步包括：
收集附加聚合度量和唯一特征数据；以及
基于现有收集的数据和附加收集的数据导出新的交互的概率和随机数学模型。
42、如权利要求31所述的方法，其中收集聚合度量数据包括：
对指定时限收集有关每个Internet协议地址的若干点击的数据；
收集有关每个用户会话的若干唯一查询的数据；
对指定时限收集有关若干网络点击的数据；
收集有关访问该网络的若干不同介绍伙伴的数据。
43、如权利要求31所述的方法，其中唯一特征数据的收集包括：
收集有关该用户的起始地址的数据；
收集有关交互的时间的数据；
收集有关交互的类型的数据；以及
收集有关该用户的唯一性的假定度量的数据。
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