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1. 发明申请

US20090265328A1 PREDICTING NEWSWORTHY QUERIES USING COMBINED ONLINE AND OFFLINE MODELS 审中-公开
标题翻译：使用组合的在线和离线模型预测新闻的疑问
公开(公告)号：US20090265328A1
公开(公告)日：2009-10-22
申请号：US12104111
申请日：2008-04-16
申请人： Rajesh Parekh , Jignashu Parikh , Pavel Berkhin
发明人： Rajesh Parekh , Jignashu Parikh , Pavel Berkhin
IPC分类号： G06F17/30
CPC分类号： G06F16/951 , G06F16/334
摘要： Methods and apparatus are described for identifying newsworthy search queries employing a machine learning approach which combines offline and online modeling to achieve a high level of accuracy as well as timeliness and scalability.
摘要翻译：描述了使用机器学习方法来识别具有新闻价值的搜索查询的方法和装置，其结合了离线和在线建模以实现高水平的准确性以及及时性和可扩展性。

2. 发明申请

US20090100051A1 DIFFERENTIATED TREATMENT OF SPONSORED SEARCH RESULTS BASED ON SEARCH CONTEXT 审中-公开
标题翻译：基于搜索条件的赞助搜索结果的差异化治疗
公开(公告)号：US20090100051A1
公开(公告)日：2009-04-16
申请号：US11870008
申请日：2007-10-10
申请人： Rushi Bhatt , Jignashu Parikh , Rajesh Girish Parekh , Pavel Berkhin
发明人： Rushi Bhatt , Jignashu Parikh , Rajesh Girish Parekh , Pavel Berkhin
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06F16/9535
摘要： Methods and apparatus are described for presenting sponsored search results. A user is enabled to initiate a search from a context. The sponsored search results and organic search results are presented in a search results page in response to the search, an order of the sponsored search results and placement of subsets of the sponsored search results relative to the organic search results in the search results page having been determined with reference to contextual information relating to the context.
摘要翻译：描述了用于呈现赞助搜索结果的方法和装置。用户可以从上下文启动搜索。赞助的搜索结果和有机搜索结果在搜索结果页面中呈现在搜索结果页面中，以搜索结果页面，搜索结果页面中的赞助搜索结果的顺序和赞助搜索结果相对于自然搜索结果的子集的位置参考与上下文相关的上下文信息确定。

3. 发明申请

US20070282785A1 Keyword set and target audience profile generalization techniques 有权
标题翻译：关键字集和目标受众概况泛化技术
公开(公告)号：US20070282785A1
公开(公告)日：2007-12-06
申请号：US11444996
申请日：2006-05-31
申请人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley
发明人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley
IPC分类号： G06F17/30
CPC分类号： G06F17/30699 , G06Q30/0207 , G06Q30/0244
摘要： A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.
摘要翻译：描述各种技术，参考关键字，简档和底层用户群体的数据之间的关系，哪些关键词集和目标受众简档可以以系统和有效的方式被概括。

4. 发明授权

US07822745B2 Keyword set and target audience profile generalization techniques 有权
标题翻译：关键字集和目标受众概况泛化技术
公开(公告)号：US07822745B2
公开(公告)日：2010-10-26
申请号：US11444996
申请日：2006-05-31
申请人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley, II
发明人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley, II
IPC分类号： G06F7/00 , G06F17/30 , G06Q30/00 , G07G1/14
CPC分类号： G06F17/30699 , G06Q30/0207 , G06Q30/0244
摘要： A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.
摘要翻译：描述各种技术，参考关键字，简档和底层用户群体的数据之间的关系，哪些关键词集和目标受众简档可以以系统和有效的方式被概括。

5. 发明申请

US20110015991A1 KEYWORD SET AND TARGET AUDIENCE PROFILE GENERALIZATION TECHNIQUES 审中-公开
标题翻译：关键词集和目标观察概况概述技术
公开(公告)号：US20110015991A1
公开(公告)日：2011-01-20
申请号：US12888256
申请日：2010-09-22
申请人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley, II
发明人： Usama M. Fayyad , Pavel Berkhin , Andrew Tomkins , Rajesh Girish Parekh , Jignashu Parikh , David Wellspring Sculley, II
IPC分类号： G06Q30/00 , G06F17/30
CPC分类号： G06Q30/0207 , G06F16/335 , G06Q30/0244
摘要： A variety of techniques are described by which keyword sets and target audience profiles may be generalized in a systematic and effective way with reference to relationships between keywords, profiles, and the data of an underlying user population.
摘要翻译：描述各种技术，参考关键字，简档和底层用户群体的数据之间的关系，哪些关键词集和目标受众简档可以以系统和有效的方式被概括。

6. 发明授权

US08762364B2 Personalizing sponsored search advertising layout using user behavior history 有权
标题翻译：使用用户行为历史个性化赞助搜索广告布局
公开(公告)号：US08762364B2
公开(公告)日：2014-06-24
申请号：US12050846
申请日：2008-03-18
申请人： Rajesh Parekh , Jignesh Parmar , Pavel Berkhin
发明人： Rajesh Parekh , Jignesh Parmar , Pavel Berkhin
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： Embodiments of the invention relate to methods of presenting personalized search results pages to users, and to search engine systems and servers configured to implement such methods. For example, a method of presenting such a page to a user of a search engine includes steps of computing an engagement index of the user based on the distribution in time of that user's interactions with the search engine then presenting, in response to a query by the user, a personalized search results page to the user.
摘要翻译：本发明的实施例涉及向用户呈现个性化搜索结果页面的方法以及被配置为实现这种方法的搜索引擎系统和服务器的方法。例如，向搜索引擎的用户呈现这样的页面的方法包括以下步骤：基于该用户与搜索引擎的交互的时间分布来计算用户的参与度索引，然后响应于以下的查询：用户，个性化搜索结果页面给用户。

7. 发明申请

US20090240677A1 Personalizing Sponsored Search Advertising Layout using User Behavior History 有权
标题翻译：使用用户行为历史个性化赞助商搜索广告版面
公开(公告)号：US20090240677A1
公开(公告)日：2009-09-24
申请号：US12050846
申请日：2008-03-18
申请人： Rajesh Parekh , Jignesh Parmar , Pavel Berkhin
发明人： Rajesh Parekh , Jignesh Parmar , Pavel Berkhin
IPC分类号： G06F17/30
CPC分类号： G06F17/30867
摘要： Embodiments of the invention relate to methods of presenting personalized search results pages to users, and to search engine systems and servers configured to implement such methods. For example, a method of presenting such a page to a user of a search engine includes steps of computing an engagement index of the user based on the distribution in time of that user's interactions with the search engine then presenting, in response to a query by the user, a personalized search results page to the user.
摘要翻译：本发明的实施例涉及向用户呈现个性化搜索结果页面的方法以及被配置为实现这种方法的搜索引擎系统和服务器的方法。例如，向搜索引擎的用户呈现这样的页面的方法包括以下步骤：基于该用户与搜索引擎的交互的时间分布来计算用户的参与度索引，然后响应于以下的查询：用户，个性化搜索结果页面给用户。

8. 发明申请

US20130346219A1 BID ESTIMATION FOR CONTEXTUAL ADVERTISEMENTS 审中-公开
标题翻译：比较广告估价
公开(公告)号：US20130346219A1
公开(公告)日：2013-12-26
申请号：US13532310
申请日：2012-06-25
申请人： Ye Chen , Pavel Berkhin , Jie Li , Xia Sharon Wan , Tak Yan
发明人： Ye Chen , Pavel Berkhin , Jie Li , Xia Sharon Wan , Tak Yan
IPC分类号： G06Q30/02
CPC分类号： G06Q30/02
摘要： Methods and systems for estimating the value of a contextual ad impression are provided. Requests for value-based bids for ad impressions are received from bidders and the value of the ad impression is estimated based primarily upon leveraging sell-side data (user and publisher). The estimation is highly economized through a fast implementation of k-nearest-neighbor (kNN) regression. Embodiments of the present invention further address the cold-start problem or the exploration vs. exploitation requirement by Bayesian (hierarchical) smoothing using a beta prior, and adapt to the temporal dynamics using an autoregressive model to decay importance of certain data.
摘要翻译：提供了用于估计内容相关广告展示价值的方法和系统。从投标人那里收到广告展示的基于价值的出价请求，主要是利用卖方数据（用户和发布商）来估计广告展示的价值。通过快速实现k-最近邻（kNN）回归，估计是高度节省的。本发明的实施例进一步解决了使用β先验的贝叶斯（分层）平滑的冷启动问题或勘探与开发要求，并且使用自回归模型适应时间动力学以衰减某些数据的重要性。

9. 发明授权

US08554622B2 Evaluating performance of binary classification systems 有权
标题翻译：评估二进制分类系统的性能
公开(公告)号：US08554622B2
公开(公告)日：2013-10-08
申请号：US11612004
申请日：2006-12-18
申请人： Richard Tao-Hwa Chow , Pavel Berkhin , Elena Eneva , Boris Klots , Nicolas Eddy Mayoraz , Rajesh Girish Parekh
发明人： Richard Tao-Hwa Chow , Pavel Berkhin , Elena Eneva , Boris Klots , Nicolas Eddy Mayoraz , Rajesh Girish Parekh
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0248 , G06Q10/0635 , G06Q30/0185 , G06Q30/02
摘要： Methods and apparatus are described for evaluating a binary classification system operable to classify each of a plurality of events as a first event type or a second event type. At least some of the events of the first event type are independently verifiable with reference to verification data. The binary classification system is susceptible to a first error type in which events of the first event type are classified as the second event type, and a second error type in which events of the second event type are classified as the first event type. Operation of a first configuration of the binary classification system is evaluated with reference to an objective function. The objective function is derived by expressing a number of errors of the second error type in terms of a number of errors of the first error type with reference to the verification data, and by assuming relative proportions of the first and second event types within the plurality of events.
摘要翻译：描述了用于评估用于将多个事件中的每一个分类为第一事件类型或第二事件类型的二进制分类系统的方法和装置。参考验证数据，可以独立地验证第一事件类型的至少一些事件。二进制分类系统易于将第一事件类型的事件分类为第二事件类型的第一错误类型和将第二事件类型的事件分类为第一事件类型的第二错误类型。参照目标函数对二进制分类系统的第一配置的操作进行评估。目标函数是通过参照验证数据表示第一错误类型的错误数量的第二错误类型的错误数，并且假设多个中的第一和第二事件类型的相对比例的事件。

10. 发明申请

US20100179855A1 Large-Scale Behavioral Targeting for Advertising over a Network 有权
标题翻译：通过网络广告的大规模行为定位
公开(公告)号：US20100179855A1
公开(公告)日：2010-07-15
申请号：US12351749
申请日：2009-01-09
申请人： Ye Chen , Dmitry Pavlov , Pavel Berkhin , John Canny
发明人： Ye Chen , Dmitry Pavlov , Pavel Berkhin , John Canny
IPC分类号： G06Q10/00 , G06F17/30 , G06F7/08 , G06N5/02
CPC分类号： G06Q30/02 , G06Q10/063 , G06Q10/0637 , G06Q10/0639 , G06Q30/0201 , G06Q30/0202 , G06Q30/0206
摘要： A method and a system are provided for large-scale behavioral targeting for advertising over a network, such as the Internet. In one example, the system receives training data that is processed raw data of user behavior. The system generates selected features by performing feature selection on the training data. The system generates feature vectors from the selected features. The system initializes weights of a behavioral targeting model by scanning the feature vectors once. The system then updates the weights of the behavioral targeting model by scanning iteratively the feature vectors using a multiplicative recurrence.
摘要翻译：提供了一种方法和系统，用于通过网络（如Internet）进行广告的大规模行为定位。在一个示例中，系统接收处理用户行为的原始数据的训练数据。系统通过对训练数据执行特征选择来生成选定的特征。系统从所选特征生成特征向量。系统通过扫描特征向量一次来初始化行为定位模型的权重。然后，系统通过使用乘法复现来迭代地扫描特征向量来更新行为定向模型的权重。

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