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    • 1. 发明授权
    • Ranking and ordering items in user-streams
    • 排序和排序用户流中的项目
    • US08874559B1
    • 2014-10-28
    • US13632308
    • 2012-10-01
    • Maryam KarimzadehganDaniel WyattAndrew Tomkins
    • Maryam KarimzadehganDaniel WyattAndrew Tomkins
    • G06F17/30
    • G06F17/30516G06F17/3053
    • Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying a set of items that are displayed to a user of a social networking service, the items comprising digital content distributed using the social networking service and being associated with item features, the item features comprising item-dependent features and user-dependent features; receiving feature values that are associated with one of an item-dependent feature and a user-dependent feature; receiving probabilities that are associated with a group and reflecting a likelihood that the user is a type of user associated with the group, the groups including a set of weights; determining an item score based on the feature values and a set of weights to provide item scores, the set of weights being identified based on the probabilities; and determining a subset of items to be displayed to the user based on the item scores.
    • 方法,系统和装置,包括在计算机存储介质上编码的计算机程序,用于识别显示给社交网络服务的用户的一组项目,所述项目包括使用社交网络服务分发的数字内容,并且与 项目特征,项目特征包括依赖于项目的特征和用户依赖特征; 接收与项目相关特征和用户相关特征之一相关联的特征值; 接收与组相关联的概率并且反映用户是与组相关联的一种类型的用户的可能性,所述组包括一组权重; 基于所述特征值和一组权重来确定项目得分以提供项目分数,所述权重集合基于所述概率被识别; 以及基于所述项目得分来确定要向用户显示的项目的子集。
    • 5. 发明授权
    • System and method for population-targeted advertising
    • 以人口为目标的广告系统和方法
    • US08452832B2
    • 2013-05-28
    • US11495269
    • 2006-07-27
    • Pavel BerkhinShanmugasundaram RavikumarAndrew TomkinsJohn Anthony Tomlin
    • Pavel BerkhinShanmugasundaram RavikumarAndrew TomkinsJohn Anthony Tomlin
    • G06F15/16G06Q30/00
    • G06F17/30702G06F17/30864G06F17/30867G06Q30/02H04L67/02H04L67/20H04L67/306
    • An improved system and method for web destination profiling for online population-targeted advertising is provided. A web destination profiler may be provided for generating web destination profiles. Traffic may be analyzed at a particular web destination in order to understand the population visiting the web destination. The analysis of user traffic, including differentiated clickstream data, may be applied for determining known characteristics of a web destination profile. Moreover, unknown characteristics of a web destination profile may be determined using a variety of techniques including inferring characteristics by modeling traffic flow through other web destinations, estimating characteristics from other web destination profiles by predicting traffic flow through other web destinations, propagating characteristics to a web destination profile by smoothing a joint distribution of characteristics of other web destination profiles, and so forth. Web destination profiles may be used by applications such as an online application for population-targeted advertising.
    • 提供了一种用于在线面向对象广告的Web目标分析的改进的系统和方法。 可以提供web目的地分析器来生成web目的地简档。 可以在特定的网络目的地分析流量,以了解访问网络目的地的人口。 用户流量的分析(包括差分点击流数据)可以被应用于确定web目的地简档的已知特征。 此外,可以使用各种技术来确定web目的地简档的未知特性,包括通过建模通过其他web目的地的业务流来推断特征,通过预​​测通过其他web目的地的业务流来估计来自其他web目的地简档的特征,将特征传播到web 通过平滑其他web目的地简档的特征的联合分布等来实现目的地简档。 Web目标配置文件可能被诸如在线应用程序的应用程序用于针对人口的广告。
    • 7. 发明授权
    • System and method using hierachical clustering for evolutionary clustering of sequential data sets
    • 使用层次聚类的序列数据集的进化聚类的系统和方法
    • US07734629B2
    • 2010-06-08
    • US11414442
    • 2006-04-29
    • Deepayan ChakrabartiShanmugasundaram RavikumarAndrew Tomkins
    • Deepayan ChakrabartiShanmugasundaram RavikumarAndrew Tomkins
    • G06F7/00
    • G06F17/30705G06K9/6218
    • An improved system and method for evolutionary clustering of sequential data sets is provided. A snapshot cost may be determined for representing the data set for a particular clustering method used and may determine the cost of clustering the data set independently of a series of clusterings of the data sets in the sequence. A history cost may also be determined for measuring the distance between corresponding clusters of the data set and the previous data set in the sequence of data sets to determine a cost of clustering the data set as part of a series of clusterings of the data sets in the sequence. An overall cost may be determined for clustering the data set by minimizing the combination of the snapshot cost and the history cost. Any clustering method may be used, including flat clustering and hierarchical clustering.
    • 提供了一种用于顺序数据集进化聚类的改进的系统和方法。 可以确定用于表示所使用的特定聚类方法的数据集的快照成本,并且可以独立于序列中的数据集的一系列聚类来确定数据集的聚类成本。 还可以确定历史成本用于测量数据集的相应簇之间的距离和数据集序列中的先前数据集之间的距离,以确定数据集的聚类成本,作为数据集的一系列聚类的一部分 序列。 可以通过最小化快照成本和历史成本的组合来确定用于对数据集进行聚类的总体成本。 可以使用任何聚类方法,包括平面聚类和层次聚类。