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公开(公告)号：US08412726B2
公开(公告)日：2013-04-02
申请号：US12793047
申请日：2010-06-03
申请人： Jun Yan , Ning Liu , Zheng Chen , Lei Ji , Jiulong Wang , Xiao Liang
发明人： Jun Yan , Ning Liu , Zheng Chen , Lei Ji , Jiulong Wang , Xiao Liang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20110302155A1
公开(公告)日：2011-12-08
申请号：US12793047
申请日：2010-06-03
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen , Jiulong Wang , Xiao Liang
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen , Jiulong Wang , Xiao Liang
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20160140254A1
公开(公告)日：2016-05-19
申请号：US14897824
申请日：2013-06-13
申请人： Jun YAN , Ning LIU , Lei JI , Zheng CHEN , MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
IPC分类号： G06F17/30 , G06F3/0484
CPC分类号： G06F17/30991 , G06F3/0484 , G06F17/3053 , G06F17/30864 , G06Q30/0629
摘要： Some examples include displaying a user interface that includes attributes and entities that are determined to be related to an input entity identified by a user. Further, some implementations include displaying a structured data table that identifies attribute values associated with the input entity and selected related entities.
摘要翻译： 一些示例包括显示包括被确定为与由用户标识的输入实体相关的属性和实体的用户界面。 此外，一些实现方式包括显示结构化数据表，其识别与输入实体和所选相关实体相关联的属性值。
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公开(公告)号：US08868598B2
公开(公告)日：2014-10-21
申请号：US13586711
申请日：2012-08-15
申请人： Jianwen Zhang , Zhimin Zhang , Jian-Tao Sun , Jun Yan , Ning Liu , Lei Ji , Weizhu Chen , Zheng Chen
发明人： Jianwen Zhang , Zhimin Zhang , Jian-Tao Sun , Jun Yan , Ning Liu , Lei Ji , Weizhu Chen , Zheng Chen
CPC分类号： G06F17/30032 , G06F17/30905
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20130138655A1
公开(公告)日：2013-05-30
申请号：US13307836
申请日：2011-11-30
申请人： Jun Yan , Lei Ji , Ning Liu , Zheng Chen
发明人： Jun Yan , Lei Ji , Ning Liu , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06F17/30702 , G06F17/30867
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US20120259801A1
公开(公告)日：2012-10-11
申请号：US13081391
申请日：2011-04-06
申请人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
发明人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
IPC分类号： G06F15/18
CPC分类号： G06N99/005
摘要： Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain.
摘要翻译： 学习的转移为根据不同任务对搜索查询进行分类的新领域提供了新的领域，以及生成可用于根据新域中的不同任务对搜索查询进行分类的相应的域特定查询分类器。 学习的转移可能包括准备一个新的域，以通过用搜索引擎日志提取的初步查询模式填充新域来从一个或多个源域接收分类知识。 学习的转移还可以包括准备每个源域中的分类知识以转移到新的域。 然后可以将每个源域中的分类知识转移到新域。
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公开(公告)号：US20100161596A1
公开(公告)日：2010-06-24
申请号：US12344093
申请日：2008-12-24
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen
CPC分类号： G06F17/30675
摘要： A tool facilitating learning latent semantics for ranking (LLSR) tailored to the ranking task via leveraging relevance information of query-document pairs to learn a tailored latent semantic space such that other documents are better ranked for the queries in the subspace. The tool applying a learning latent semantics for ranking algorithm integrating LLSR, thereby enabling learning an optimal latent semantic space (LSS) for ranking by utilizing relevance information in the training process of subspace learning. The tool enabling an optimization of the LSS as a closed form solution and facilitating reporting the learned LSS.
摘要翻译： 一种通过利用查询文档对的相关性信息来学习定制的潜在语义空间，使其他文档更好地排列在子空间中的查询的方法，帮助学习用于排名任务的潜在语义（LLSR）。 该工具应用学习潜在语义用于整合LLSR的排序算法，从而通过在子空间学习的训练过程中利用相关性信息来学习优化潜在语义空间（LSS）进行排名。 该工具可以将LSS优化为封闭式解决方案，并有助于报告所学习的LSS。
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公开(公告)号：US08719192B2
公开(公告)日：2014-05-06
申请号：US13081391
申请日：2011-04-06
申请人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
发明人： Lei Ji , Jun Yan , Ning Liu , Zheng Chen
CPC分类号： G06N99/005
摘要： Transfer of learning trains a new domain for the classification of search queries according to different tasks, as well as the generation of a corresponding domain-specific query classifier that may be used to classify the search queries according to the different tasks in the new domain. The transfer of learning may include preparing a new domain to receive classification knowledge from one or more source domains by populating the new domain with preliminary query patterns extracted for a search engine log. The transfer of learning may further include preparing the classification knowledge in each source domain for transfer to the new domain. The classification knowledge in each source domain may then be transferred to the new domain.
摘要翻译： 学习的转移为根据不同任务对搜索查询进行分类的新领域提供了新的领域，以及生成可用于根据新域中的不同任务对搜索查询进行分类的相应的域特定查询分类器。 学习的转移可能包括准备一个新的域，以通过用搜索引擎日志提取的初步查询模式填充新域来从一个或多个源域接收分类知识。 学习的转移还可以包括准备每个源域中的分类知识以转移到新的域。 然后可以将每个源域中的分类知识转移到新域。
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公开(公告)号：US20130073546A1
公开(公告)日：2013-03-21
申请号：US13235140
申请日：2011-09-16
申请人： Jun Yan , Ning Liu , Lei Ji , Steven J. Hanks , Qing Xu , Zheng Chen
发明人： Jun Yan , Ning Liu , Lei Ji , Steven J. Hanks , Qing Xu , Zheng Chen
IPC分类号： G06F17/30
CPC分类号： G06F17/30321 , G06F17/30867
摘要： 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.
摘要翻译： 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。
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公开(公告)号：US07958125B2
公开(公告)日：2011-06-07
申请号：US12146481
申请日：2008-06-26
申请人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen , Jian Wang
发明人： Jun Yan , Ning Liu , Lei Ji , Zheng Chen , Jian Wang
CPC分类号： G06F17/30705
摘要： A method for merging really simple syndication (RSS) feeds. Stories containing one or more terms may be merged into one or more clusters based on one or more links between the stories. A cluster frequency with which the terms occur in each cluster may be determined. A diameter for each cluster may be determined. A cluster that is most similar to one of the clusters may be determined based on the cluster frequency. The most similar cluster with the one of the clusters may be determined based on each diameter, and each cluster frequency.
摘要翻译： 一种合并真正简单的联合（RSS）馈送的方法。 包含一个或多个术语的故事可以基于故事之间的一个或多个链接合并成一个或多个集群。 可以确定在每个簇中出现术语的聚类频率。 可以确定每个簇的直径。 可以基于群集频率来确定与簇之一最相似的群集。 可以基于每个直径和每个聚类频率来确定具有一个簇的最相似的簇。
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