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1. 发明申请

US20120123993A1 Action Prediction and Identification Temporal User Behavior 有权
标题翻译：行动预测和识别时间用户行为
公开(公告)号：US20120123993A1
公开(公告)日：2012-05-17
申请号：US12947871
申请日：2010-11-17
申请人： Gang Wang , Weizhu Chen , Zheng Chen , Zhikai Fan
发明人： Gang Wang , Weizhu Chen , Zheng Chen , Zhikai Fan
IPC分类号： G06N7/02
CPC分类号： G06N5/02 , G06F17/30693 , G06N5/043 , G06Q30/00 , G06Q30/0202
摘要： User behavior modeling can include determining temporal- or time-based actions performed by various users. From the mined temporal-based user actions, future actions can be predicted. Certain implementations include providing information and/or services based on the predicted future actions. Some implementations, include providing relevant information, services, and/or goods regarding the predicted future action.
摘要翻译：用户行为建模可以包括确定由各种用户执行的时间或时间的动作。从开采的基于时间的用户操作，可以预测未来的行动。某些实施方式包括基于预测的未来行动来提供信息和/或服务。一些实施方式包括提供有关预测未来行动的相关信息，服务和/或商品。

2. 发明授权

US08589228B2 Click modeling for URL placements in query response pages 有权
标题翻译：点击查询响应页面中的网址展示位置的建模
公开(公告)号：US08589228B2
公开(公告)日：2013-11-19
申请号：US12795631
申请日：2010-06-07
申请人： Weizhu Chen , Gang Wang , Zheng Chen , Zhikai Fan , Thomas Minka
发明人： Weizhu Chen , Gang Wang , Zheng Chen , Zhikai Fan , Thomas Minka
IPC分类号： G06F15/18 , G06F17/60 , G06Q30/00 , G06N5/02
CPC分类号： G06Q30/02 , G06Q30/0255
摘要： A “General Click Model” (GCM) is constructed using a Bayesian network that is inherently capable of modeling “tail queries” by building the model on multiple attribute values that are shared across queries. More specifically, the GCM learns and predicts user click behavior towards URLs displayed on a query results page returned by a search engine. Unlike conventional click modeling approaches that learn models based on individual queries, the GCM learns click models from multiple attributes, with the influence of different attribute values being measured by Bayesian inference. This provides an advantage in learning that enables the GCM to achieve improved generalization and results, especially for tail queries, than conventional click models. In addition, most conventional click models consider only position and the identity of URLs when learning the model. In contrast, the GCM considers more session-specific attributes in making a final prediction for anticipated or expected user click behaviors.
摘要翻译：使用贝叶斯网络构建“通用点击模型”（GCM），该贝叶斯网络本质上能够通过在查询之间共享的多个属性值上建立模型来建模“尾部查询”。更具体地说，GCM学习并预测用户对搜索引擎返回的查询结果页面上显示的URL的点击行为。不同于传统的点击建模方法，基于个别查询的模型，GCM从多个属性学习点击模型，不同属性值的影响是通过贝叶斯推理来衡量的。这提供了学习的优势，使得GCM能够实现改进的泛化和结果，特别是尾部查询，而不是传统的点击模型。此外，大多数传统的点击模型只在学习模型时考虑URL的位置和身份。相比之下，GCM考虑更多的会话特定属性来对预期或预期的用户点击行为进行最终预测。

3. 发明授权

US08412665B2 Action prediction and identification temporal user behavior 有权
标题翻译：行动预测和识别临时用户行为
公开(公告)号：US08412665B2
公开(公告)日：2013-04-02
申请号：US12947871
申请日：2010-11-17
申请人： Gang Wang , Weizhu Chen , Zheng Chen , Zhikai Fan
发明人： Gang Wang , Weizhu Chen , Zheng Chen , Zhikai Fan
IPC分类号： G06F9/44 , G06N7/02 , G06N7/06
CPC分类号： G06N5/02 , G06F17/30693 , G06N5/043 , G06Q30/00 , G06Q30/0202
摘要： User behavior modeling can include determining temporal- or time-based actions performed by various users. From the mined temporal-based user actions, future actions can be predicted. Certain implementations include providing information and/or services based on the predicted future actions. Some implementations, include providing relevant information, services, and/or goods regarding the predicted future action.
摘要翻译：用户行为建模可以包括确定由各种用户执行的时间或时间的动作。从开采的基于时间的用户操作，可以预测未来的行动。某些实施方式包括基于预测的未来行动来提供信息和/或服务。一些实施方式包括提供有关预测未来行动的相关信息，服务和/或商品。

4. 发明申请

US20110302031A1 CLICK MODELING FOR URL PLACEMENTS IN QUERY RESPONSE PAGES 有权
标题翻译：点击建模查询响应页面中的网址
公开(公告)号：US20110302031A1
公开(公告)日：2011-12-08
申请号：US12795631
申请日：2010-06-07
申请人： Weizhu Chen , Gang Wang , Zheng Chen , Zhikai Fan , Thomas Minka
发明人： Weizhu Chen , Gang Wang , Zheng Chen , Zhikai Fan , Thomas Minka
IPC分类号： G06F15/18 , G06Q30/00 , G06N5/02
CPC分类号： G06Q30/02 , G06Q30/0255
摘要： A “General Click Model” (GCM) is constructed using a Bayesian network that is inherently capable of modeling “tail queries” by building the model on multiple attribute values that are shared across queries. More specifically, the GCM learns and predicts user click behavior towards URLs displayed on a query results page returned by a search engine. Unlike conventional click modeling approaches that learn models based on individual queries, the GCM learns click models from multiple attributes, with the influence of different attribute values being measured by Bayesian inference. This provides an advantage in learning that enables the GCM to achieve improved generalization and results, especially for tail queries, than conventional click models. In addition, most conventional click models consider only position and the identity of URLs when learning the model. In contrast, the GCM considers more session-specific attributes in making a final prediction for anticipated or expected user click behaviors.
摘要翻译：使用贝叶斯网络构建“通用点击模型”（GCM），该贝叶斯网络本质上能够通过在查询之间共享的多个属性值上构建模型来建模“尾部查询”。更具体地说，GCM学习并预测用户对搜索引擎返回的查询结果页面上显示的URL的点击行为。不同于传统的点击建模方法，基于个别查询的模型，GCM从多个属性学习点击模型，不同属性值的影响是通过贝叶斯推理来衡量的。这提供了学习的优势，使得GCM能够实现改进的泛化和结果，特别是尾部查询，而不是传统的点击模型。此外，大多数传统的点击模型只在学习模型时考虑URL的位置和身份。相比之下，GCM考虑更多的会话特定属性来对预期或预期的用户点击行为进行最终预测。

5. 发明申请

US20120143789A1 CLICK MODEL THAT ACCOUNTS FOR A USER'S INTENT WHEN PLACING A QUIERY IN A SEARCH ENGINE 审中-公开
标题翻译：在搜索引擎中放置校园时，用户的信息的点击模式
公开(公告)号：US20120143789A1
公开(公告)日：2012-06-07
申请号：US12957521
申请日：2010-12-01
申请人： Gang Wang , Weizhu Chen , Zheng Chen
发明人： Gang Wang , Weizhu Chen , Zheng Chen
IPC分类号： G06F17/30 , G06F15/18 , G06N5/02
CPC分类号： G06F16/951
摘要： A method of generating training data for a search engine begins by retrieving log data pertaining to user click behavior. The log data is analyzed based on a click model that includes a parameter pertaining to a user intent bias representing the intent of a user in performing a search in order to determine a relevance of each of a plurality of pages to a query. The relevance of the pages is then converted into training data.
摘要翻译：生成搜索引擎的训练数据的方法从检索与用户点击行为有关的日志数据开始。基于点击模型分析日志数据，所述点击模型包括与用户意图偏差相关的参数，所述参数表示用户执行搜索的意图，以便确定多个页面中的每一个与查询的相关性。然后将页面的相关性转换为训练数据。

6. 发明申请

US20120143790A1 RELEVANCE OF SEARCH RESULTS DETERMINED FROM USER CLICKS AND POST-CLICK USER BEHAVIOR OBTAINED FROM CLICK LOGS 审中-公开
标题翻译：从用户点击确定的搜索结果的相关性和点击记录获得的点击后用户行为
公开(公告)号：US20120143790A1
公开(公告)日：2012-06-07
申请号：US12957692
申请日：2010-12-01
申请人： Gang Wang , Weizhu Chen , Zheng Chen
发明人： Gang Wang , Weizhu Chen , Zheng Chen
IPC分类号： G06N5/02 , G06F15/18
CPC分类号： G06N7/005 , G06F16/951
摘要： Data from a click log may be used to generate training data for a search engine. User click behavior and user post-click behavior may be used to assess the relevance of a page to a query. Labels for training data may be generated based on data from the click log. The labels may pertain to the relevance of a page to a query. For example, user post-click behavior that may be examined includes the amount of time that a user remains on a target page when a user clicks one of the search results.
摘要翻译：来自点击日志的数据可用于生成搜索引擎的训练数据。用户单击行为和用户点击后行为可用于评估页面与查询的相关性。可以根据点击日志的数据生成训练数据的标签。标签可能与页面与查询的相关性有关。例如，可以检查的用户点击后行为包括当用户单击其中一个搜索结果时用户保留在目标页面上的时间量。

7. 发明授权

US08626677B2 Training SVMs with parallelized stochastic gradient descent 有权
标题翻译：用并行随机梯度下降训练SVM
公开(公告)号：US08626677B2
公开(公告)日：2014-01-07
申请号：US12790532
申请日：2010-05-28
申请人： Weizhu Chen , Gang Wang , Zheng Chen
发明人： Weizhu Chen , Gang Wang , Zheng Chen
IPC分类号： G06F15/18
CPC分类号： G06K9/6269 , G06N99/005
摘要： Techniques for training a non-linear support vector machine utilizing a stochastic gradient descent algorithm are provided. The computations of the stochastic gradient descent algorithm are parallelized via a number of processors. Calculations of the stochastic gradient descent algorithm on a particular processor may be combined according to a packing strategy before communicating the results of the calculations with the other processors.
摘要翻译：提供了利用随机梯度下降算法训练非线性支持向量机的技术。随机梯度下降算法的计算通过多个处理器并行化。在将计算结果与其他处理器通信之前，可以根据打包策略来组合特定处理器上的随机梯度下降算法。

8. 发明申请

US20110295774A1 Training SVMs with Parallelized Stochastic Gradient Descent 有权
标题翻译：训练具有并行随机梯度下降的SVM
公开(公告)号：US20110295774A1
公开(公告)日：2011-12-01
申请号：US12790532
申请日：2010-05-28
申请人： Weizhu Chen , Gang Wang , Zheng Chen
发明人： Weizhu Chen , Gang Wang , Zheng Chen
IPC分类号： G06F15/18
CPC分类号： G06K9/6269 , G06N99/005
摘要： Techniques for training a non-linear support vector machine utilizing a stochastic gradient descent algorithm are provided. The computations of the stochastic gradient descent algorithm are parallelized via a number of processors. Calculations of the stochastic gradient descent algorithm on a particular processor may be combined according to a packing strategy before communicating the results of the calculations with the other processors.
摘要翻译：提供了利用随机梯度下降算法训练非线性支持向量机的技术。随机梯度下降算法的计算通过多个处理器并行化。在将计算结果与其他处理器通信之前，可以根据打包策略来组合特定处理器上的随机梯度下降算法。

9. 发明授权

US09430521B2 Query expansion through searching content identifiers 有权
标题翻译：通过搜索内容标识符查询扩展
公开(公告)号：US09430521B2
公开(公告)日：2016-08-30
申请号：US12570948
申请日：2009-09-30
申请人： Zheng Chen , Gang Wang , Jian-Tao Sun , Xiaochuan Ni
发明人： Zheng Chen , Gang Wang , Jian-Tao Sun , Xiaochuan Ni
IPC分类号： G06F17/30
CPC分类号： G06F17/30392 , G06F17/30572
摘要： Techniques and technologies for expanding a gallery by seeding the gallery with seed query results. A template is identified which is associated with the subjects of the seed queries and content identifiers are identified which include the template. These content identifiers are used to expand the gallery to include more content identifiers than before the expansion of the gallery.
摘要翻译：通过种子查询结果种植画廊来扩大画廊的技术和技术。识别与种子查询的主题相关联的模板，并且识别包括模板的内容标识符。这些内容标识符用于展开图库以包括比展开图库之前更多的内容标识符。

10. 发明申请

US20110078193A1 QUERY EXPANSION THROUGH SEARCHING CONTENT IDENTIFIERS 有权
标题翻译：通过搜索内容标识符查询扩展
公开(公告)号：US20110078193A1
公开(公告)日：2011-03-31
申请号：US12570948
申请日：2009-09-30
申请人： Zheng Chen , Gang Wang , Jian-Tao Sun , Xiaochuan Ni
发明人： Zheng Chen , Gang Wang , Jian-Tao Sun , Xiaochuan Ni
IPC分类号： G06F17/30
CPC分类号： G06F17/30392 , G06F17/30572
摘要： Techniques and technologies for expanding a gallery by seeding the gallery with seed query results. A template is identified which is associated with the subjects of the seed queries and content identifiers are identified which include the template. These content identifiers are used to expand the gallery to include more content identifiers than before the expansion of the gallery.
摘要翻译：通过种子查询结果种植画廊来扩大画廊的技术和技术。识别与种子查询的主题相关联的模板，并且识别包括模板的内容标识符。这些内容标识符用于展开图库以包括比展开图库之前更多的内容标识符。

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