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    • 4. 发明授权
    • Methods and apparatus for determining social relevance in near constant time
    • 近一段时间内确定社会相关性的方法和装置
    • US07856449B1
    • 2010-12-21
    • US11123853
    • 2005-05-06
    • Paul J. MartinoChris ValeKristopher C. Wehner
    • Paul J. MartinoChris ValeKristopher C. Wehner
    • G06F7/00G06F17/30
    • G06F17/30979G06F17/3089G06F17/30958G06F17/30997G06Q50/01H04L67/10H04L67/306
    • A computer system includes a database configured to store a plurality of social network relationships, a graphing system coupled to a database, wherein the graphing system includes a processor and random access memory, wherein the random access memory is configured to store at least a portion of the plurality of social network relationships from the database, wherein the processor is configured to determine a social map for a user in response to at least the portion of the plurality of social network relationships in the random access memory, and wherein the random access memory is configured to store the social map for the user, and a server coupled to the database and the graphing system, wherein the server is configured to receive an indication of the user, and wherein the server is configured to provide the indication of the user to the graphing system.
    • 计算机系统包括被配置为存储多个社交网络关系的数据库,耦合到数据库的图形系统,其中所述图形系统包括处理器和随机存取存储器,其中所述随机存取存储器被配置为存储至少一部分 来自数据库的多个社交网络关系,其中所述处理器被配置为响应于所述随机存取存储器中的所述多个社交网络关系的至少一部分来确定用户的社交地图,并且其中所述随机存取存储器是 被配置为存储用于用户的社交地图,以及耦合到数据库和图形系统的服务器,其中服务器被配置为接收用户的指示,并且其中服务器被配置为向用户提供用户的指示, 绘图系统
    • 6. 发明申请
    • METHODS AND APPARATUS FOR INTEGRATING SOCIAL NETWORK METRICS AND REPUTATION DATA
    • 用于整合社会网络量度和信誉数据的方法和装置
    • US20110219073A1
    • 2011-09-08
    • US13110302
    • 2011-05-18
    • Brian LawlerElliot LohPaul J. MartinoMark Pincus
    • Brian LawlerElliot LohPaul J. MartinoMark Pincus
    • G06F15/16
    • H04L51/32G06F15/16H04L51/12
    • A method for a computer system includes determining a first social distance for a first user with respect to a second user, determining a second social distance for a third user with respect to the second user, determining a first qualitative rating associated with the first user, determining a second qualitative rating associated with the second user, determining a first trust-metric for the first user in response to the first social distance and the first qualitative rating, determining a second trust-metric for the third user in response to the second social distance and the second qualitative rating, and prioritizing a first listing from the first user over a second listing from the third user for the second user, in response to the first trust-metric and the second trust-metric.
    • 一种用于计算机系统的方法包括确定第一用户相对于第二用户的第一社交距离,确定第三用户相对于第二用户的第二社交距离,确定与第一用户相关联的第一定性评级, 确定与所述第二用户相关联的第二定性评级,响应于所述第一社交距离和所述第一定性评级确定所述第一用户的第一信任度量,响应于所述第二社会距离确定所述第三用户的第二信任度量 距离和第二定性评级,以及响应于第一信任度量和第二信任度量,从第一用户通过第三用户针对第二用户的第二列表对第一列表进行优先级排序。
    • 7. 发明授权
    • Using cross-site relationships to generate recommendations
    • 使用跨站点关系来生成建议
    • US07788358B2
    • 2010-08-31
    • US11369562
    • 2006-03-06
    • Paul J. Martino
    • Paul J. Martino
    • G06F15/173
    • G06F17/30702G06F17/30867G06Q30/0255G06Q30/0633
    • A relationship server tracks end-user interactions across multiple web sites and generates recommendations. The web sites observe relationships established by end-user interactions. If end-users provide the same personally identifiable information to multiple web sites, the sites generate the same unique identifier for those end-users. The web sites send messages to the relationship server that reference the end-users using the identifiers and describe the relationships observed for the end-users. The relationship server receives messages from multiple web sites and canonicalizes them to produce an efficient representation of the relationships. Upon receiving a message requesting a recommendation based on an item, the relationship server performs collaborative filtering using the relationship data to identify a list of items to recommend. The relationship server sends the recommendations to the requesting entity and the recommendations are presented to the end-user.
    • 关系服务器跟踪多个网站之间的最终用户交互,并生成建议。 网站观察由最终用户互动建立的关系。 如果最终用户向多个网站提供相同的个人身份信息,那么这些站点会为这些终端用户生成相同的唯一标识符。 网站将信息发送给使用标识符引用最终用户的关系服务器,并描述最终用户观察到的关系。 关系服务器从多个网站接收消息,并对它们进行规范化,以产生关系的有效表示。 在接收到基于项目的请求推荐的消息时,关系服务器使用关系数据执行协作过滤以识别要推荐的项目的列表。 关系服务器将建议发送给请求实体,并将建议呈现给最终用户。