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公开(公告)号：US20100241597A1
公开(公告)日：2010-09-23
申请号：US12407785
申请日：2009-03-19
Applicant: Bee-Chung Chen , Pradheep Elango , Deepak K. Agarwal , Wei Chu
Inventor： Bee-Chung Chen , Pradheep Elango , Deepak K. Agarwal , Wei Chu
CPC classification number: G06Q30/02 , G06F16/958
Abstract: Techniques are presented for estimating the current popularity of web content. Click and view data for articles are used to estimate popularity of the articles by analyzing click-through rates. Click-though rates are estimated such that a current click-through rate reflects fluctuations in popularity of articles through time.
Abstract translation: 介绍了估计网页内容当前流行度的技术。 点击查看文章数据用于通过分析点击率来估算文章的受欢迎程度。 点击率估算，使得当前的点击率反映了文章随着时间的流行度的波动。
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公开(公告)号：US09519682B1
公开(公告)日：2016-12-13
申请号：US13117037
申请日：2011-05-26
Applicant: Jay Pujara , Vishwanath Tumkur Ramarao , Xiaopeng Xi , Martin Zinkevich , Anirban Dasgupta , Belle Tseng , Wei Chu , Jyh-Shin Gareth Shue
Inventor： Jay Pujara , Vishwanath Tumkur Ramarao , Xiaopeng Xi , Martin Zinkevich , Anirban Dasgupta , Belle Tseng , Wei Chu , Jyh-Shin Gareth Shue
CPC classification number: G06F17/3053 , G06Q10/10 , H04H20/28 , H04H60/82
Abstract: Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.
Abstract translation: 实施例旨在通过用户帐户可信度得分来生成统一的用户帐户可信赖性系统。 可以基于针对给定动作的每个用户帐户的可信度分数将多个用户帐户分组成层，可以为给定动作识别可信赖的用户帐户组。 层级和/或可信赖性分数可以用于基于来自用户帐户的输入来将项目（诸如作为垃圾邮件或非垃圾邮件）的消息分类。 还可以使用可信度分数来确定用户帐户是机器人帐户还是人类账户。 给定动作的受信任组可以通过基于修改的可信度得分重新分组用户账户而随着时间的推移而动态演变。 可以基于从单个用户帐户接收的输入和来自其他用户帐户的输入来修改个人用户帐户的可信度分数。
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公开(公告)号：US08560293B2
公开(公告)日：2013-10-15
申请号：US13569728
申请日：2012-08-08
Applicant: H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Inventor： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC: G06F17/50
CPC classification number: G06F17/3089
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US08244517B2
公开(公告)日：2012-08-14
申请号：US12267534
申请日：2008-11-07
Applicant: H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Inventor： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC: G06F9/45
CPC classification number: G06F17/3089
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US20100121624A1
公开(公告)日：2010-05-13
申请号：US12267534
申请日：2008-11-07
Applicant: H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Inventor： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC: G06G7/48
CPC classification number: G06F17/3089
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US20130111005A1
公开(公告)日：2013-05-02
申请号：US13282285
申请日：2011-10-26
Applicant: Wei Chu , Martin Zinkevich , Lihong Li , Achint Oommen Thomas , Belle Tseng
Inventor： Wei Chu , Martin Zinkevich , Lihong Li , Achint Oommen Thomas , Belle Tseng
Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.
Abstract translation: 用于在线主动学习的软件会收到发布到网站上的在线流的内容。 软件将内容转换为元素表示，并将元素表示输入到概率模型中，以获得内容滥用的预测概率。 该软件还基于元素表示计算重要性权重。 并且如果满足条件，则软件使用内容，重要性权重以及获取的标签来更新概率模型。 条件取决于工具分配。 如果满足条件，该软件将从在线流中删除内容。 如果获取的标签不可用，则条件取决于预测概率。
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公开(公告)号：US20120303349A1
公开(公告)日：2012-11-29
申请号：US13569728
申请日：2012-08-08
Applicant: H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Inventor： H. Scott Roy , Raghunath Ramakrishnan , Pradheep Elango , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
IPC: G06G7/62
CPC classification number: G06F17/3089
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US20100121801A1
公开(公告)日：2010-05-13
申请号：US12267538
申请日：2008-11-07
Applicant: H. Scott Roy , Raghunath Ramakirshnan , Pardheep Elgano , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Inventor： H. Scott Roy , Raghunath Ramakirshnan , Pardheep Elgano , Nitin Motgi , Deepak K. Agarwal , Wei Chu , Bee-Chung Chen
Abstract: 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.
Abstract translation: 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。
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公开(公告)号：US10019518B2
公开(公告)日：2018-07-10
申请号：US12577045
申请日：2009-10-09
Applicant: Jiang Chen , Wei Chu , Zhenzhen Kou , Zhaohui Zheng
Inventor： Jiang Chen , Wei Chu , Zhenzhen Kou , Zhaohui Zheng
CPC classification number: G06F16/951
Abstract: Methods and systems are disclosed that relate to ranking functions for multiple different domains. By way of example but not limitation, ranking functions for multiple different domains may be trained based on inter-domain loss, and such ranking functions may be used to rank search results from multiple different domains so that they may be blended without normalizing relevancy scores.
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公开(公告)号：US09967218B2
公开(公告)日：2018-05-08
申请号：US13282285
申请日：2011-10-26
Applicant: Wei Chu , Martin Zinkevich , Lihong Li , Achint Oommen Thomas , Belle Tseng
Inventor： Wei Chu , Martin Zinkevich , Lihong Li , Achint Oommen Thomas , Belle Tseng
Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.
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