公开(公告)号：US08370337B2

公开(公告)日：2013-02-05

申请号：US12762929

申请日：2010-04-19

申请人： Tapas Kanungo ,  Kumaresh Pattabiraman ,  Nitin Agrawal ,  Kieran Richard McDonald ,  Christopher Avery Meyers ,  Nipoon Malhotra

发明人： Tapas Kanungo ,  Kumaresh Pattabiraman ,  Nitin Agrawal ,  Kieran Richard McDonald ,  Christopher Avery Meyers ,  Nipoon Malhotra

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06N99/005 ,  G06F17/30882

摘要： Methods and computer-storage media having computer-executable instructions embodied thereon that facilitate generating a machine-learned model for ranking search results using click-based data are provided. Data is referenced from user queries, which may include search results generated by general search engines and vertical search engines. A training set is generated from the search results and click-based judgments are associated with the search results in the training set. Based on click-based judgments, identifiable features are determined from the search results in a training set. Based on determining identifiable features in a training set, a rule set is generated for ranking subsequent search results.

摘要翻译： 提供了具有其上包含计算机可执行指令的方法和计算机存储介质，其利用基于点击的数据便于生成用于对搜索结果进行排名的机器学习模型。 数据来自用户查询，可能包括由一般搜索引擎和垂直搜索引擎生成的搜索结果。 从搜索结果生成训练集，并且基于点击的判断与训练集中的搜索结果相关联。 基于点击判断，可以从训练集中的搜索结果确定可识别的特征。 基于确定训练集中的可识别特征，生成用于对后续搜索结果进行排序的规则集。

公开(公告)号：US08150841B2

公开(公告)日：2012-04-03

申请号：US12690184

申请日：2010-01-20

申请人： Christopher Avery Meyers ,  Gopi Prashanth Gopal ,  Andrew Peter Oakley ,  Nitin Agrawal ,  Nicholas Eric Craswell ,  Milad Shokouhi ,  Derrick Leslie Connell ,  Sanaz Ahari ,  Neil Bruce Sharman ,  Gaurav Sareen ,  Hugh Evan Williams ,  Jay Kumar Goyal

发明人： Christopher Avery Meyers ,  Gopi Prashanth Gopal ,  Andrew Peter Oakley ,  Nitin Agrawal ,  Nicholas Eric Craswell ,  Milad Shokouhi ,  Derrick Leslie Connell ,  Sanaz Ahari ,  Neil Bruce Sharman ,  Gaurav Sareen ,  Hugh Evan Williams ,  Jay Kumar Goyal

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06F17/30448 ,  G06Q30/0254

摘要： Methods, systems, and media are provided for identifying and clustering queries that are rising in popularity. Resultant clustered queries can be compared to other stored queries using textual and temporal correlations. Fresh indices containing information and results from recently crawled content sources are searched to obtain the most recent query activity. Historical indices are also searched to obtain temporally correlated information and results that match the clustered query stream. A weighted average acceleration of a spike can be calculated to distinguish between a legitimate spike and a non-legitimate spike. Legitimate clusters are combined with other stored clusters and presented as grouped content results to a user output device.

摘要翻译： 提供了方法，系统和媒体，用于识别和聚集正在日益普及的查询。 可以使用文本和时间相关性将所产生的聚类查询与其他存储的查询进行比较。 搜索包含最近爬取的内容源的信息和结果的新索引以获取最近的查询活动。 还搜索历史索引以获得与聚集查询流匹配的时间相关信息和结果。 可以计算穗的加权平均加速度，以区分合法穗和非合法穗。 合法集群与其他存储的集群组合，并以分组的内容结果呈现给用户输出设备。