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1. 发明申请

US20090063984A1 Customized today module 有权
标题翻译：定制今天模块
公开(公告)号：US20090063984A1
公开(公告)日：2009-03-05
申请号：US11899401
申请日：2007-09-04
申请人： Deepak Agarwal , Bee-Chung Chen , Pradheep Elango , Nitin Motgi , ViJay K. Narayanan , Raghu Ramakrishnan , Howard Scott Roy , Amitabh Seth , Vik Singh , Joe Zachariah , Sharat Israni , John Thrall , Chandar Venkataraman , Amit Phadke , Michael Salisbury
发明人： Deepak Agarwal , Bee-Chung Chen , Pradheep Elango , Nitin Motgi , ViJay K. Narayanan , Raghu Ramakrishnan , Howard Scott Roy , Amitabh Seth , Vik Singh , Joe Zachariah , Sharat Israni , John Thrall , Chandar Venkataraman , Amit Phadke , Michael Salisbury
IPC分类号： G06F3/048
CPC分类号： G06Q30/02
摘要： A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.
摘要翻译：提供了一种用于定制呈现给个体用户或用户段的内容的方法和装置。可能有三个组件，Web门户和工具栏组件，建模组件和评分组件。网络门户和工具栏组件提供内容项目并收集数据。 Web门户和工具栏组件基于用户操作生成用户事件数据。用户事件数据被转发到建模组件。建模组件基于用户事件数据和内容项的属性生成内容评分函数。内容评分功能可能对于各个用户段是唯一的。基于内容特征的内容评分功能产生内容项目将被查看的概率。评分组件决定在门户中放置哪些内容项目。评分组件使用建模组件生成的评分函数实时对内容项进行排名。

2. 发明授权

US08065619B2 Customized today module 有权
标题翻译：定制今天模块
公开(公告)号：US08065619B2
公开(公告)日：2011-11-22
申请号：US11899401
申请日：2007-09-04
申请人： Deepak Agarwal , Bee-Chung Chen , Pradheep Elango , Nitin Motgi , Vijay K. Narayanan , Raghu Ramakrishnan , Howard Scott Roy , Amitabh Seth , Vik Singh , Joe Zachariah , Sharat Israni , John Thrall , Chandar Venkataraman , Amit Phadke , Michael Salisbury
发明人： Deepak Agarwal , Bee-Chung Chen , Pradheep Elango , Nitin Motgi , Vijay K. Narayanan , Raghu Ramakrishnan , Howard Scott Roy , Amitabh Seth , Vik Singh , Joe Zachariah , Sharat Israni , John Thrall , Chandar Venkataraman , Amit Phadke , Michael Salisbury
IPC分类号： G06F3/00
CPC分类号： G06Q30/02
摘要： A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.
摘要翻译：提供了一种用于定制呈现给个体用户或用户段的内容的方法和装置。可能有三个组件，Web门户和工具栏组件，建模组件和评分组件。网络门户和工具栏组件提供内容项目并收集数据。 Web门户和工具栏组件基于用户操作生成用户事件数据。用户事件数据被转发到建模组件。建模组件基于用户事件数据和内容项的属性生成内容评分函数。内容评分功能可能对于各个用户段是唯一的。基于内容特征的内容评分功能产生内容项目将被查看的概率。评分组件决定在门户中放置哪些内容项目。评分组件使用建模组件生成的评分函数实时对内容项进行排名。

3. 发明申请

US20120084155A1 PRESENTATION OF CONTENT BASED ON UTILITY 审中-公开
标题翻译：基于实用程序的内容呈现
公开(公告)号：US20120084155A1
公开(公告)日：2012-04-05
申请号：US12895868
申请日：2010-10-01
申请人： Scott Roy , Belle Tseng , Pradheep Elango , Bee-Chung Chen , Jayavel Shanmugasundaram , Raghu Ramakrishnan , Andrei Broder , Deepak Agarwal , Todd Beaupre , Nitin Motgi , John Tomlin
发明人： Scott Roy , Belle Tseng , Pradheep Elango , Bee-Chung Chen , Jayavel Shanmugasundaram , Raghu Ramakrishnan , Andrei Broder , Deepak Agarwal , Todd Beaupre , Nitin Motgi , John Tomlin
IPC分类号： G06Q30/00 , G06N5/02
CPC分类号： G06F3/04817 , G06F3/0482 , G06F17/212 , G06Q30/0273
摘要： Methods and systems for presenting content such as articles based on utility are provided. In one embodiment, a plurality of articles are determined, each article in the plurality of articles including article content and a corresponding preview icon, the preview icon defining a link to the corresponding article content when presented. For each article in the plurality of articles, a user experience utility value is determined. And for each article in the plurality of articles, an economic utility value is also determined. A ranked order of the articles is determined based upon each article's user experience utility value and economic utility value. And a portion of the preview icons of the articles are presented on a graphical display page in a priority orientation based on the ranked order of the articles.
摘要翻译：提供了基于实用程序呈现内容的方法和系统，例如文章。在一个实施例中，确定多个物品，所述多个物品中的每个物品包括物品内容和相应的预览图标，所述预览图标在呈现时定义到相应的物品内容的链接。对于多个文章中的每篇文章，确定用户体验效用值。对于多篇文章中的每篇文章，也确定了经济效用价值。根据每篇文章的用户体验效用值和经济效用值确定文章的排序顺序。并且文章的预览图标的一部分基于文章的排序顺序以优先方向呈现在图形显示页面上。

4. 发明授权

US08504558B2 Framework to evaluate content display policies 有权
标题翻译：评估内容显示政策的框架
公开(公告)号：US08504558B2
公开(公告)日：2013-08-06
申请号：US12184114
申请日：2008-07-31
申请人： Deepak Agarwal , Pradheep Elango , Raghu Ramakrishnan , Seung-Taek Park , Bee-Chung Chen
发明人： Deepak Agarwal , Pradheep Elango , Raghu Ramakrishnan , Seung-Taek Park , Bee-Chung Chen
IPC分类号： G06F17/30
CPC分类号： G06Q30/02
摘要： Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.
摘要翻译：使用两种方法评估内容显示策略。在第一种方法中，使用以“受控”的方式收集关于用户特征和内容特征的信息，生成真实模型。模拟器会根据真实模型重播用户对门户网页的访问，并模拟与页面上的内容项目的交互。各种指标用于比较不同的内容项目选择算法。在第二种方法中，没有建立明确的真理模型。受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。衡量总体预测误差的度量用于比较不同的内容项目选择算法。以受控方式收集的数据在这两种方法中起关键作用。

5. 发明申请

US20100030717A1 FRAMEWORK TO EVALUATE CONTENT DISPLAY POLICIES 有权
标题翻译：评估内容显示政策的框架
公开(公告)号：US20100030717A1
公开(公告)日：2010-02-04
申请号：US12184114
申请日：2008-07-31
申请人： Deepak Agarwal , Pradheep Elango , Raghu Ramakrishnan , Seung-Taek Park , Bee-Chung Chen
发明人： Deepak Agarwal , Pradheep Elango , Raghu Ramakrishnan , Seung-Taek Park , Bee-Chung Chen
IPC分类号： G06N5/02
CPC分类号： G06Q30/02
摘要： Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a “controlled” manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.
摘要翻译：使用两种方法评估内容显示策略。在第一种方法中，使用以“受控”的方式收集关于用户特征和内容特征的信息，生成真实模型。模拟器会根据真实模型重播用户对门户网页的访问，并模拟与页面上的内容项目的交互。各种指标用于比较不同的内容项目选择算法。在第二种方法中，没有建立明确的真理模型。受控服务计划的活动部分或全部重播; 内容项目选择算法学习使用观察到的用户活动。衡量总体预测误差的度量用于比较不同的内容项目选择算法。以受控方式收集的数据在这两种方法中起关键作用。

6. 发明申请

US20120303349A1 ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES 有权
标题翻译：通过探索/开发计划进行更好的匹配
公开(公告)号：US20120303349A1
公开(公告)日：2012-11-29
申请号：US13569728
申请日：2012-08-08
申请人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
发明人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC分类号： G06G7/62
CPC分类号： G06F17/3089
摘要： Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
摘要翻译：内容项被选择以在门户页面上显示，以便最大化诸如点击率的性能度量。解决与内容选择相关的问题，例如改变内容池，可变性能度量，以及一旦项目已被显示给用户，对项目的反馈的延迟。用于多武装强盗问题的基于优先权的方案的适应性用于预测未来数据趋势。适应性将关于未来时间段的实验引入计算，这增加了解决多武装强盗问题的数据集。此外，贝叶斯探索/漏洞利用方法被制定为一个优化问题，解决门户页面的内容项目选择的所有问题。该优化问题由拉格朗日弛豫和正态逼近法进行修正，可实时计算优化问题。

7. 发明授权

US08560293B2 Enhanced matching through explore/exploit schemes 有权
标题翻译：通过探索/利用方案增强匹配
公开(公告)号：US08560293B2
公开(公告)日：2013-10-15
申请号：US13569728
申请日：2012-08-08
申请人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
发明人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC分类号： G06F17/50
CPC分类号： G06F17/3089
摘要： Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
摘要翻译：内容项被选择以在门户页面上显示，以便最大化诸如点击率的性能度量。解决与内容选择相关的问题，例如改变内容池，可变性能度量，以及一旦项目已被显示给用户，对项目的反馈的延迟。用于多武装强盗问题的基于优先权的方案的适应性用于预测未来数据趋势。适应性将关于未来时间段的实验引入计算，这增加了解决多武装强盗问题的数据集。此外，贝叶斯探索/漏洞利用方法被制定为一个优化问题，解决门户页面的内容项目选择的所有问题。该优化问题由拉格朗日弛豫和正态逼近法进行修正，可实时计算优化问题。

8. 发明授权

US08244517B2 Enhanced matching through explore/exploit schemes 有权
标题翻译：通过探索/利用方案增强匹配
公开(公告)号：US08244517B2
公开(公告)日：2012-08-14
申请号：US12267534
申请日：2008-11-07
申请人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
发明人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC分类号： G06F9/45
CPC分类号： G06F17/3089
摘要： Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.
摘要翻译：内容项被选择以在门户页面上显示，以便最大化诸如点击率的性能度量。解决与内容选择相关的问题，例如改变内容池，可变性能度量，以及一旦项目已被显示给用户，对项目的反馈的延迟。用于多武装强盗问题的基于优先权的方案的改编用于预测未来数据趋势。适应性将关于未来时间段的实验引入计算，这增加了解决多武装强盗问题的数据集。此外，贝叶斯探索/漏洞利用方法被制定为一个优化问题，解决门户页面的内容项目选择的所有问题。该优化问题由拉格朗日弛豫和正态逼近法进行修正，可实时计算优化问题。

9. 发明申请

US20100121624A1 ENHANCED MATCHING THROUGH EXPLORE/EXPLOIT SCHEMES 有权
公开(公告)号：US20100121624A1
公开(公告)日：2010-05-13
申请号：US12267534
申请日：2008-11-07
申请人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
发明人： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC分类号： G06G7/48
CPC分类号： G06F17/3089
摘要： Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

10. 发明申请

US20100125585A1 Conjoint Analysis with Bilinear Regression Models for Segmented Predictive Content Ranking 审中-公开
标题翻译：用于分段预测内容排名的双线性回归模型的联合分析
公开(公告)号：US20100125585A1
公开(公告)日：2010-05-20
申请号：US12272607
申请日：2008-11-17
申请人： Wei Chu , Seung-Taek Park , Raghu Ramakrishnan , Bee-Chung Chen , Deepak K. Agarwal , Pradheep Elango , Scott Roy , Todd Beaupre
发明人： Wei Chu , Seung-Taek Park , Raghu Ramakrishnan , Bee-Chung Chen , Deepak K. Agarwal , Pradheep Elango , Scott Roy , Todd Beaupre
IPC分类号： G06F17/30 , G06F7/06 , G06F7/00
CPC分类号： G06F16/3346 , G06F16/313
摘要： Information with respect to users, items, and interactions between the users and items is collected. Each user is associated with a set of user features. Each item is associated with a set of item features. An expected score function is defined for each user-item pair, which represents an expected score a user assigns an item. An objective represents the difference between the expected score and the actual score a user assigns an item. The expected score function and the objective function share at least one common variable. The objective function is minimized to find best fit for some of the at least one common variable. Subsequently, the expected score function is used to calculate expected scores for individual users or clusters of users with respect to a set of items that have not received actual scores from the users. The set of items are ranked based on their expected scores.
摘要翻译：收集关于用户，项目以及用户和项目之间的交互的信息。每个用户与一组用户特征相关联。每个项目与一组项目特征相关联。为每个用户 - 物品对定义预期分数函数，其表示用户分配项目的预期分数。目标表示用户分配项目的预期分数与实际分数之间的差异。预期得分函数和目标函数共享至少一个共同变量。目标函数被最小化以找到最适合至少一个共同变量中的一些。随后，使用预期分数函数来计算相对于尚未从用户那里获得实际分数的一组项目的个体用户或用户群的预期分数。该组项目根据其预期分数进行排名。

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