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    • 83. 发明授权
    • Web knowledge extraction for search task simplification
    • Web知识提取用于搜索任务简化
    • US09020947B2
    • 2015-04-28
    • US13307836
    • 2011-11-30
    • Jun YanLei JiNing LiuZheng Chen
    • Jun YanLei JiNing LiuZheng Chen
    • G06F7/00G06F17/30
    • G06F17/30702G06F17/30867
    • Techniques are described for generating structured information from semi-structured web pages, and retrieving the structured knowledge in response to a user query that indicates a query intent. The structured information is automatically extracted offline from semi-structured web pages, through the use of an auto wrapper solution that is noise tolerant, scalable, and automatic. The structured information is stored in a knowledge base, and provided in response to a user search query that indicates a query intent. Extraction of structured information may also include clustering of pages based on their measured similarities. The clusters may be determined based on similar elements in the tag path text data of the pages. A minimum size threshold may be applied to the clusters.
    • 描述了用于从半结构化网页生成结构化信息的技术,以及响应于指示查询意图的用户查询来检索结构化知识。 结构化信息通过使用具有噪声容限,可扩展和自动的自动包装解决方案,从半结构化网页离线自动提取。 结构化信息存储在知识库中,并响应于指示查询意图的用户搜索查询而提供。 结构化信息的提取还可以包括基于其测量的相似性来聚合页面。 可以基于页面的标签路径文本数据中的类似元素来确定簇。 可以将最小大小阈值应用于群集。
    • 84. 发明申请
    • SMART USER-CENTRIC INFORMATION AGGREGATION
    • SMART用户中心信息聚合
    • US20140052751A1
    • 2014-02-20
    • US13586711
    • 2012-08-15
    • Jianwen ZhangZhimin ZhangJian-Tao SunJun YanNing LiuLei JiWeizhu ChenZheng Chen
    • Jianwen ZhangZhimin ZhangJian-Tao SunJun YanNing LiuLei JiWeizhu ChenZheng Chen
    • G06F17/30
    • G06F17/30032G06F17/30905
    • A smart user-centric information aggregation system allows a user to define a region of content displayed in a display of a device and performs information aggregation on behalf of the user. The smart user-centric information aggregation system searches, aggregates and groups information related to content included in the region of content for the user while the user can continue to perform his/her original course of actions without interruption. After finding information related to the desired content, the smart user-centric information aggregation system may notify the user and present the found information to the user upon receiving confirmation from the user. The smart user-centric information aggregation system may continue to find new related information and update the presentation with the newly found information periodically, in some instances without user intervention or input.
    • 以智能用户为中心的信息聚合系统允许用户定义显示在设备显示器中的内容区域,并代表用户执行信息聚合。 智能用户为中心的信息聚合系统在用户可以继续执行他/她的原始行为过程而不间断地搜索,聚合和分组与用户内容区域中包含的内容相关的信息。 在找到与期望内容相关的信息之后,智能用户为中心的信息聚合系统可以在接收到来自用户的确认时通知用户并向用户呈现找到的信息。 以智能用户为中心的信息聚合系统可以继续寻找新的相关信息,并且在某些情况下,不需要用户干预或输入,定期更新新发现的信息。
    • 85. 发明授权
    • Indexing semantic user profiles for targeted advertising
    • 索引用于定向广告的语义用户配置文件
    • US08533188B2
    • 2013-09-10
    • US13235140
    • 2011-09-16
    • Jun YanNing LiuLei JiSteven J. HanksQing XuZheng Chen
    • Jun YanNing LiuLei JiSteven J. HanksQing XuZheng Chen
    • G06F17/30
    • G06F17/30321G06F17/30867
    • Embodiments facilitate greater flexibility in definition of user segments for targeted advertising, by employing indexed semantic user profiles. Semantic user profiles are built through extraction of online user behavior data such as user search queries and page views, and include user interest information that is inferred based on user behavior. Semantic user profiles are then indexed to facilitate search for a set of users that fit specified semantic search terms. Search results for semantic profiles are ranked according to a ranking model developed through machine learning. In some embodiments, building and indexing of semantic profiles and learning of the ranking model is performed offline to facilitate more efficient online processing of queries.
    • 实施例通过采用索引语义用户简档来促进用于定向广告的用户段的定义的更大的灵活性。 通过提取在线用户行为数据(如用户搜索查询和页面浏览)构建语义用户配置文件,并包括基于用户行为推断的用户兴趣信息。 然后索引语义用户简档,以便于搜索适合指定语义搜索术语的一组用户。 根据通过机器学习开发的排名模型对语义轮廓的搜索结果进行排名。 在一些实施例中,离线地执行语义概况的构建和索引以及排名模型的学习,以便更有效地在线处理查询。
    • 88. 发明申请
    • RELATED LINKS RECOMMENDATION
    • 相关链接建议
    • US20110302155A1
    • 2011-12-08
    • US12793047
    • 2010-06-03
    • Jun YanNing LiuLei JiZheng ChenJiulong WangXiao Liang
    • Jun YanNing LiuLei JiZheng ChenJiulong WangXiao Liang
    • G06F17/30
    • G06F17/30867
    • The related links recommendation technique described herein employs combined collaborative filtering to recommend related web pages to users. The technique creates multiple collaborative filters which are combined in order to create a combined collaborative filter to recommend web pages similar to a given web page to a user. One query-based collaborative filter is created by using query search clicks (e.g., user input device selection actions on search results returned in response to a search query). Another user-behavior-based collaborative filter is created by using query search clicks and user clicks while browsing websites (e.g., user input device selection actions while a user is browsing websites). Lastly, another content-based collaborative filter based on similar content of web pages is created by finding web pages with similar content.
    • 本文描述的相关链接推荐技术采用组合协同过滤来向用户推荐相关网页。 该技术创建了多个协作过滤器,这些过滤器被组合以便创建组合的协同过滤器以向用户推荐类似于给定网页的网页。 通过使用查询搜索点击创建一个基于查询的协作过滤器(例如,响应于搜索查询返回的搜索结果上的用户输入设备选择动作)。 通过在浏览网站时使用查询搜索点击和用户点击创建另一个基于用户行为的协作过滤器(例如,用户浏览网站时的用户输入设备选择动作)。 最后,通过查找具有相似内容的网页来创建基于类似内容的网页的另一基于内容的协作过滤器。
    • 89. 发明申请
    • Scalable Parallel User Clustering in Discrete Time Window
    • 离散时间窗口中可扩展的并行用户群集
    • US20100169258A1
    • 2010-07-01
    • US12346881
    • 2008-12-31
    • Jun YanNing LiuLei JiZheng Chen
    • Jun YanNing LiuLei JiZheng Chen
    • G06N5/02G06F7/06G06F17/30
    • G06F16/9535
    • Described is an internet user clustering technology, such as useful in behavioral targeting, in which users are clustered together based on MinHash computations that produce signatures corresponding to users' internet-related activities. In one aspect, users are clustered together based on commonality of signatures between each set of signatures associated with each user. The signature sets and/or clusters may be associated with timestamps, whereby clusters may be determined for a given discrete time window or set of discrete time windows. To facilitate efficient processing, existing, prior signature sets of a user may be incrementally updated (e.g., daily), and/or the MinHash computations for users are partitioned among parallel computing machines. The timestamps may be used to selectively determine a cluster within a continuous time, a time window or set of time windows.
    • 描述了一种互联网用户聚类技术,例如在行为定位中是有用的,其中基于MinHash计算将用户聚集在一起,该计算产生对应于用户的互联网相关活动的签名。 在一个方面,用户基于与每个用户相关联的每组签名之间的签名的共性来聚集在一起。 签名集合和/或聚类可以与时间戳相关联,由此可以针对给定的离散时间窗口或一组离散时间窗口确定聚类。 为了促进有效的处理,用户的现有的先前签名集可以被递增地更新(例如,每天),和/或用于用户的MinHash计算在并行计算机之间被划分。 时间戳可以用于在连续时间,时间窗口或一组时间窗口内选择性地确定群集。
    • 90. 发明申请
    • SEARCHING HETEROGENEOUS INTERRELATED ENTITIES
    • 搜索异构中介实体
    • US20080215565A1
    • 2008-09-04
    • US11853613
    • 2007-09-11
    • Jian-Tao SunXuanhui WangNing LiuZheng ChenJian Wang
    • Jian-Tao SunXuanhui WangNing LiuZheng ChenJian Wang
    • G06F17/30
    • G06F17/30867
    • Systems and methods for searching heterogeneous interrelated entities for a heterogeneous entities search query are disclosed herein. A user may enter the heterogeneous entities search query. The search retrieves and returns multiple types of heterogeneous entities. The retrieved heterogeneous interrelated entities are searched in a unified matrix that represents relationships between one or more heterogeneous entities. The retrieved heterogeneous interrelated entities may have one or more entity types. The set of retrieved interrelated entities may also be ranked based on the similarity between each entity and the search query. Feedback may also be incorporated into the system to improve search accuracy.
    • 本文公开了用于异构实体搜索查询的异构相关实体的搜索的系统和方法。 用户可以输入异构实体搜索查询。 搜索检索并返回多种类型的异构实体。 检索到的异构相关实体在表示一个或多个异构实体之间的关系的统一矩阵中进行搜索。 所检索的异构相关实体可以具有一个或多个实体类型。 还可以根据每个实体和搜索查询之间的相似性对所检索的相关实体的集合进行排名。 反馈也可以并入系统以提高搜索精度。