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1. 发明申请

US20100125570A1 CLICK MODEL FOR SEARCH RANKINGS 有权
标题翻译：点击模式搜索排名
公开(公告)号：US20100125570A1
公开(公告)日：2010-05-20
申请号：US12273425
申请日：2008-11-18
申请人： Olivier Chapelle , Anne Ya Zhang
发明人： Olivier Chapelle , Anne Ya Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30864
摘要： Approaches and techniques are discussed for ranking the documents indicated in search results for a query based on click-through information collected for the query in previous query sessions. According to an embodiment of the invention, when calculating a relevance score for a particular document, one may overcome positional bias by utilizing click-through information about other documents previously returned in the same search results as the particular document. According to an embodiment, one may utilize Dynamic Bayesian Network, based on said click-through information, to model relevance. According to an embodiment of the invention, one may utilize click-through information to generate targets for learning a ranking function.
摘要翻译：讨论方法和技术，用于根据在以前的查询会话中为查询收集的点击信息对查询的搜索结果中指示的文档进行排名。根据本发明的实施例，当计算特定文档的相关性得分时，可以通过利用与特定文档相同的搜索结果中先前返回的其他文档的点击信息来克服位置偏差。根据实施例，可以基于所述点击信息来利用动态贝叶斯网络来模拟相关性。根据本发明的实施例，可以利用点击信息来生成用于学习排名功能的目标。

2. 发明授权

US08671093B2 Click model for search rankings 有权
标题翻译：点击型号搜索排名
公开(公告)号：US08671093B2
公开(公告)日：2014-03-11
申请号：US12273425
申请日：2008-11-18
申请人： Olivier Chapelle , Anne Ya Zhang
发明人： Olivier Chapelle , Anne Ya Zhang
IPC分类号： G06F17/30
CPC分类号： G06F17/30864
摘要： Approaches and techniques are discussed for ranking the documents indicated in search results for a query based on click-through information collected for the query in previous query sessions. According to an embodiment of the invention, when calculating a relevance score for a particular document, one may overcome positional bias by utilizing click-through information about other documents previously returned in the same search results as the particular document. According to an embodiment, one may utilize Dynamic Bayesian Network, based on said click-through information, to model relevance. According to an embodiment of the invention, one may utilize click-through information to generate targets for learning a ranking function.
摘要翻译：讨论方法和技术，用于根据在以前的查询会话中为查询收集的点击信息对查询的搜索结果中指示的文档进行排名。根据本发明的实施例，当计算特定文档的相关性得分时，可以通过利用与特定文档相同的搜索结果中先前返回的其他文档的点击信息来克服位置偏差。根据实施例，可以基于所述点击信息来利用动态贝叶斯网络来模拟相关性。根据本发明的实施例，可以利用点击信息来产生用于学习排名功能的目标。

3. 发明申请

US20110071965A1 SYSTEM AND METHOD FOR CROSS DOMAIN LEARNING FOR DATA AUGMENTATION 有权
标题翻译：用于数据接收的跨域学习的系统和方法
公开(公告)号：US20110071965A1
公开(公告)日：2011-03-24
申请号：US12566270
申请日：2009-09-24
申请人： Bo Long , Belle Tseng , Sudarshan Lamkhede , Srinivas Vadrevu , Anne Ya Zhang
发明人： Bo Long , Belle Tseng , Sudarshan Lamkhede , Srinivas Vadrevu , Anne Ya Zhang
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06N99/005 , H04L51/12
摘要： According to an example embodiment, a method comprises executing instructions by a special purpose computing apparatus to, for labeled source domain data having a plurality of original labels, generate a plurality of first predicted labels for the labeled source domain data using a target function, the target function determined by using a plurality of labels from labeled target domain data. The method further comprises executing instructions by the special purpose computing apparatus to apply a label relation function to the first predicted labels for the source domain data and the original labels for the source domain data to determine a plurality of weighting factors for the labeled source domain data. The method further comprises executing instructions by the special purpose computing apparatus to generate a new target function using the labeled target domain data, the labeled source domain data, and the weighting factors for the labeled source domain data, and evaluate a performance of the new target function to determine if there is a convergence.
摘要翻译：根据示例性实施例，一种方法包括执行专用计算装置的指令，对于具有多个原始标签的标记源域数据，使用目标函数为标记的源域数据生成多个第一预测标签，通过使用来自标记的目标域数据的多个标签确定目标函数。该方法还包括由专用计算装置执行指令以对源域数据的第一预测标签和源域数据的原始标签应用标签关系函数，以确定用于标记的源域数据的多个权重因子。该方法还包括执行专用计算装置的指令，以使用标记的目标域数据，标记的源域数据和标记的源域数据的加权因子来生成新的目标函数，并评估新目标的性能确定是否存在收敛的功能。

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