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1. 发明申请

US20100306161A1 CLICK THROUGH RATE PREDICTION USING A PROBABILISTIC LATENT VARIABLE MODEL 审中-公开
标题翻译：使用概率偏差变量模型点击通过率预测
公开(公告)号：US20100306161A1
公开(公告)日：2010-12-02
申请号：US12474668
申请日：2009-05-29
申请人： Ye Chen , Dmitry Pavlov , John Canny , Eren Manavoglu
发明人： Ye Chen , Dmitry Pavlov , John Canny , Eren Manavoglu
IPC分类号： G06N7/02 , G06Q30/00 , G06N5/02 , G06Q10/00 , G06F17/30 , G06F15/18
CPC分类号： G06N7/005 , G06Q30/02 , G06Q30/0242 , G06Q30/0254 , G06Q30/0256
摘要： Methods and systems are provided for predicting click through rate in connection with a particular user, keyword-based query, and advertisement using a probabilistic latent variable model. Click through rate may be predicted based on historical sponsored search activity information. Predicted click through rate may be used as a factor in determining advertisement rank.
摘要翻译：提供了方法和系统，用于使用概率潜变量模型来预测与特定用户相关联的点击率，基于关键字的查询和广告。点击率可以根据历史赞助搜索活动信息进行预测。预测点击率可用作确定广告排名的因素。

2. 发明申请

US20080172399A1 SYSTEM AND METHOD FOR AUTOMATICALLY ORGANIZING BOOKMARKS THROUGH THE USE OF TAG DATA 有权
标题翻译：通过使用标签数据自动组织书签的系统和方法
公开(公告)号：US20080172399A1
公开(公告)日：2008-07-17
申请号：US11624072
申请日：2007-01-17
申请人： Liang-Yu Chi , Dmitry Yurievich Pavlov , Yun Fu , Eren Manavoglu , Paul Heymann , Zhichen Xu
发明人： Liang-Yu Chi , Dmitry Yurievich Pavlov , Yun Fu , Eren Manavoglu , Paul Heymann , Zhichen Xu
IPC分类号： G06F17/30
CPC分类号： G06F17/30884
摘要： The present invention is directed towards systems and method for organization of bookmarks. The method according to one embodiment comprises retrieving one or more bookmarks associated with one or more content items, a given bookmark generated by a user of a client device and identifying one or more tags associated with one or uniform resource locators corresponding to the or more bookmarks. A bookmark folder hierarchy is created through use of a clustering algorithm on the basis of the one or more tags associated with the one or more uniform resource locators corresponding to the one or more bookmarks.
摘要翻译：本发明涉及用于组织书签的系统和方法。根据一个实施例的方法包括检索与一个或多个内容项目相关联的一个或多个书签，由客户端设备的用户生成的给定书签，以及识别与一个或多个与该书签相对应的一个或多个统一资源定位符相关联的一个或多个标签。基于与一个或多个书签相对应的一个或多个统一资源定位符相关联的一个或多个标签，通过使用聚类算法来创建书签文件夹层次结构。

3. 发明申请

US20080154858A1 System for targeting data to sites referenced on a page 有权
标题翻译：将数据定位到页面引用的站点的系统
公开(公告)号：US20080154858A1
公开(公告)日：2008-06-26
申请号：US11644176
申请日：2006-12-21
申请人： Eren Manavoglu , Alexandrin Popescul , Byron Dom , Cliff Brunk
发明人： Eren Manavoglu , Alexandrin Popescul , Byron Dom , Cliff Brunk
IPC分类号： G06F7/06
CPC分类号： G06Q30/02 , G06Q30/0241
摘要： A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.
摘要翻译：描述了基于条件将数据定向到页面上引用的站点的系统。该系统可以包括处理器，存储器和接口。存储器可以可操作地连接到处理器和接口，并且可以存储包含内容的数据，站点，条件和页面。接口可以可操作地连接到存储器和处理器，并且可以将页面传送给用户。处理器可以识别数据，站点，条件和包含内容的页面。如果页面的内容满足条件，处理器可以将数据添加到页面。

4. 发明授权

US08364525B2 Using clicked slate driven click-through rate estimates in sponsored search 有权
标题翻译：在赞助搜索中使用点击式平板驱动的点击率估算
公开(公告)号：US08364525B2
公开(公告)日：2013-01-29
申请号：US12956496
申请日：2010-11-30
申请人： Divy Kothiwal , Kannan Achan , Eren Manavoglu , Erick Cantu-Paz
发明人： Divy Kothiwal , Kannan Achan , Eren Manavoglu , Erick Cantu-Paz
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0256 , G06Q30/0241 , G06Q30/0251
摘要： A computer-implemented method and system for selecting a subject advertisement in a sponsored search system based on a user's commercial intent (pertaining to the subject advertisement), using techniques for determining intent-driven clicks from a historical database. The method includes steps for aggregating a training model dataset wherein the training model dataset contains a selected history of clicks. Then, selecting from the training model dataset, a clicked slate (further selection of clicks), the clicked slate comprising a set of clicked ads, and calculating an intent-driven click feedback value for the subject advertisement. The method includes techniques for selecting a clicked slate using features corresponding to clicks received within a particular time period (the time period determined statically or dynamically). A system for implementing the method includes aggregating data from a historical database using selectors such as a position selector, a click feature selector, an impression-advertiser-campaign-creative selector, and a commercial intent selector.
摘要翻译：一种计算机实现的方法和系统，用于使用用于从历史数据库确定意图驱动的点击的技术，基于用户的商业意图（涉及主题广告）在赞助搜索系统中选择主题广告。该方法包括用于聚合训练模型数据集的步骤，其中训练模型数据集包含所选择的点击历史。然后，从训练模型数据集中选择点击的图表（进一步选择点击次数），点击的图片包括一组点击的广告，以及计算该主题广告的意图驱动的点击反馈值。该方法包括使用与在特定时间段（静态或动态确定的时间段）内接收的点击对应的特征来选择点击的平板的技术。用于实现该方法的系统包括使用诸如位置选择器，点击特征选择器，展示广告商 - 广告系列创意选择器和商业意图选择器之类的选择器来汇总来自历史数据库的数据。

5. 发明申请

US20110270672A1 Ad Relevance In Sponsored Search 审中-公开
标题翻译：广告相关性在赞助搜索
公开(公告)号：US20110270672A1
公开(公告)日：2011-11-03
申请号：US12769446
申请日：2010-04-28
申请人： Dustin Hillard , Hema Raghavan , Eren Manavoglu , Chris Leggetter , Stefan Schroedl
发明人： Dustin Hillard , Hema Raghavan , Eren Manavoglu , Chris Leggetter , Stefan Schroedl
IPC分类号： G06Q30/00 , G06F15/18
CPC分类号： G06Q30/02 , G06Q30/0243 , G06Q30/0246
摘要： Techniques for improving advertisement relevance for sponsored search advertising. The method includes steps for processing a click history data structure containing at least a plurality of query-advertisement pairs, populating a first translation table containing a co-occurrence count field, populating a second translation table containing an expected clicks field, and calculating a click propensity score for an advertisement using the click history data structure, the first translation table (for determining overall click likelihood across all historical traffic), and using the second translation table (for removing biases present in the first translation table). Other method steps calculate a second click propensity score for a second advertisement, then ranking the first advertisement relative to the second advertisement for comparing a click propensity score to a threshold for filtering low quality ad candidates from a plurality of ad candidates, and then ranking advertisements for optimizing placement of ads on a sponsored search display page.
摘要翻译：用于提高赞助搜索广告广告相关性的技术。该方法包括处理包含至少多个查询 - 广告对的点击历史数据结构的步骤，填充包含同现计数字段的第一翻译表，填充包含预期点击字段的第二翻译表，以及计算点击用于使用点击历史数据结构的广告的倾向得分，第一翻译表（用于确定所有历史流量中的整体点击可能性）以及使用第二转换表（用于去除存在于第一翻译表中的偏差）。其他方法步骤计算第二广告的第二点击倾向得分，然后相对于第二广告对第一广告进行排名，用于将点击倾向得分与用于从多个广告候选中过滤低质量广告候选的阈值进行比较，然后排列广告用于优化广告在赞助的搜索显示页面上的展示位置。

6. 发明授权

US08484077B2 Using linear and log-linear model combinations for estimating probabilities of events 有权
标题翻译：使用线性和对数线性模型组合来估计事件的概率
公开(公告)号：US08484077B2
公开(公告)日：2013-07-09
申请号：US12893939
申请日：2010-09-29
申请人： Ozgur Cetin , Eren Manavoglu , Kannan Achan , Erick Cantu-Paz , Rukmini Iyer
发明人： Ozgur Cetin , Eren Manavoglu , Kannan Achan , Erick Cantu-Paz , Rukmini Iyer
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0277 , G06Q10/04 , G06Q30/0241
摘要： A method for combining multiple probability of click models in an online advertising system into a combined predictive model, the method commencing by receiving a feature set slice (e.g. corresponding to demographics or taxonomies or clusters), and using the sliced data for training multiple slice-wise predictive models. The trained slice-wise predictive models are combined by overlaying a weighted distribution model over the trained slice-wise predictive models. The combined predictive model then is used in predicting the probability of a click given a query-advertisement pair in online advertising. The method can flexibly receive slice specifications, and can overlay any one or more of a variety of distribution models, such as a linear combination or a log-linear combination. Using an appropriate weighted distribution model, the combined predictive model reliably yields predictive estimates of occurrence of click events that are at least as good as the best predictive model in the slice-wise predictive model set.
摘要翻译：一种将在线广告系统中的点击模型的多种概率组合成组合预测模型的方法，该方法通过接收特征集切片（例如，对应于人口统计学或分类或群集）开始，并且使用分片数据来训练多个切片 - 明智的预测模型。训练的切片预测模型通过在训练的切片预测模型上重叠加权分布模型来组合。然后，组合预测模型用于预测在线广告中给予查询广告对的点击的概率。该方法可以灵活地接收切片规格，并且可以覆盖各种分布模型中的任何一个或多个，例如线性组合或对数线性组合。使用适当的加权分布模型，组合预测模型可靠地产生至少与切片预测模型集中的最佳预测模型一样好的点击事件发生的预测估计。

7. 发明授权

US08108390B2 System for targeting data to sites referenced on a page 有权
标题翻译：将数据定位到页面引用的站点的系统
公开(公告)号：US08108390B2
公开(公告)日：2012-01-31
申请号：US11644176
申请日：2006-12-21
申请人： Eren Manavoglu , Alexandrin Popescul , Byron Dom , Cliff Brunk
发明人： Eren Manavoglu , Alexandrin Popescul , Byron Dom , Cliff Brunk
IPC分类号： G06F17/30
CPC分类号： G06Q30/02 , G06Q30/0241
摘要： A system is described for targeting data to a site referenced on a page based on a condition. The system may include a processor, a memory, and an interface. The memory may be operatively connected to the processor and the interface and may store a data, a site, a condition, and a page containing content. The interface may be operatively connected to the memory and the processor and may communicate the page to a user. The processor may identify the data, site, condition, and page containing content. The processor may add the data to the page if the content of the page satisfies the condition.
摘要翻译：描述了基于条件将数据定向到页面上引用的站点的系统。该系统可以包括处理器，存储器和接口。存储器可以可操作地连接到处理器和接口，并且可以存储包含内容的数据，站点，条件和页面。接口可以可操作地连接到存储器和处理器，并且可以将页面传送给用户。处理器可以识别数据，站点，条件和包含内容的页面。如果页面的内容满足条件，处理器可以将数据添加到页面。

8. 发明申请

US20110276391A1 EXPANSION OF TERM SETS FOR USE IN ADVERTISEMENT SELECTION 审中-公开
标题翻译：扩展用于广告选择的期限
公开(公告)号：US20110276391A1
公开(公告)日：2011-11-10
申请号：US12774471
申请日：2010-05-05
申请人： Dustin Hillard , Chris Leggetter , Eren Manavoglu
发明人： Dustin Hillard , Chris Leggetter , Eren Manavoglu
IPC分类号： G06Q30/00 , G06Q10/00
CPC分类号： G06Q30/02 , G06Q30/0244 , G06Q30/0246
摘要： Techniques are provided for use in online advertisement selection in response to a search query. Techniques are provided in which historical online advertising information is obtained. Segmentation is performed of advertisements and queries and used in generating segment pairs, and an associated advertisement performance is determined for each pair. Segmentation is also performed of a particular query and a candidate advertisement for selection to be served in response, and using the resulting segments, pairs are identified and used in adding to a term set associated with the candidate advertisement, which term set is used in assessing the advertisement for selection.
摘要翻译：技术被提供用于响应于搜索查询的在线广告选择。提供了获得历史在线广告信息的技术。对广告和查询执行分段，并用于生成段对，并为每对确定相关的广告演出。还对特定查询和候选广告执行分段以供选择以作出响应，并且使用所得到的分段，对对被识别并用于添加与候选广告相关联的术语集合，该术语集合用于评估广告选择。

9. 发明授权

US08010532B2 System and method for automatically organizing bookmarks through the use of tag data 有权
标题翻译：通过使用标签数据自动组织书签的系统和方法
公开(公告)号：US08010532B2
公开(公告)日：2011-08-30
申请号：US11624072
申请日：2007-01-17
申请人： Liang-Yu Chi , Dmitry Yurievich Pavlov , Yun Fu , Eren Manavoglu , Paul Heymann , Zhichen Xu
发明人： Liang-Yu Chi , Dmitry Yurievich Pavlov , Yun Fu , Eren Manavoglu , Paul Heymann , Zhichen Xu
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F17/30884
摘要： The present invention is directed towards systems and method for organization of bookmarks. The method according to one embodiment comprises retrieving one or more bookmarks associated with one or more content items, a given bookmark generated by a user of a client device and identifying one or more tags associated with one or uniform resource locators corresponding to the or more bookmarks. A bookmark folder hierarchy is created through use of a clustering algorithm on the basis of the one or more tags associated with the one or more uniform resource locators corresponding to the one or more bookmarks.
摘要翻译：本发明涉及用于组织书签的系统和方法。根据一个实施例的方法包括检索与一个或多个内容项目相关联的一个或多个书签，由客户端设备的用户生成的给定书签，以及识别与一个或多个与该书签相对应的一个或多个统一资源定位符相关联的一个或多个标签。基于与一个或多个书签相对应的一个或多个统一资源定位符相关联的一个或多个标签，通过使用聚类算法来创建书签文件夹层次结构。

10. 发明申请

US20110131157A1 SYSTEM AND METHOD FOR PREDICTING CONTEXT-DEPENDENT TERM IMPORTANCE OF SEARCH QUERIES 审中-公开
标题翻译：用于预测搜索查询的背景相关重要性的系统和方法
公开(公告)号：US20110131157A1
公开(公告)日：2011-06-02
申请号：US12626892
申请日：2009-11-28
申请人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
发明人： Rukmini Iyer , Eren Manavoglu , Hema Raghavan
IPC分类号： G06F17/30 , G06F15/18
CPC分类号： G06Q30/0251
摘要： An improved system and method for identifying context-dependent term importance of queries is provided. A query term importance model is learned using supervised learning of context-dependent term importance for queries and is then applied for advertisement prediction using term importance weights of query terms as query features. For instance, a query term importance model for query rewriting may predict rewritten queries that match a query with term importance weights assigned as query features. Or a query term importance model for advertisement prediction may predict relevant advertisements for a query with term importance weights assigned as query features. In an embodiment, a sponsored advertisement selection engine selects sponsored advertisements scored by a query term importance engine that applies a query term importance model using term importance weights as query features and inverse document frequency weights as advertisement features to assign a relevance score.
摘要翻译：提供了一种用于识别查询的上下文相关项重要性的改进的系统和方法。使用对查询的上下文相关项重要性的监督学习来学习查询词重要性模型，然后将其用作查询词语的重要度权重作为查询特征应用于广告预测。例如，用于查询重写的查询项重要性模型可以预测与查询匹配的重写查询与被分配为查询特征的术语重要性权重。或者用于广告预测的查询词重要性模型可以预测具有被指定为查询特征的术语重要性权重的查询的相关广告。在一个实施例中，赞助的广告选择引擎选择由查询词语重要性引擎评分的赞助广告，该查询词语重要性引擎使用术语重要性权重作为查询特征和逆文档频率权重作为广告特征来分配相关性得分。

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