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。

公开(公告)号：US08301624B2

公开(公告)日：2012-10-30

申请号：US12416036

申请日：2009-03-31

申请人： Seung-Taek Park ,  Wei Chu ,  Todd Beaupre ,  Deepak K. Agarwal ,  Scott Roy ,  Raghu Ramakrishnan

发明人： Seung-Taek Park ,  Wei Chu ,  Todd Beaupre ,  Deepak K. Agarwal ,  Scott Roy ,  Raghu Ramakrishnan

IPC分类号： G06F17/30

CPC分类号： G06F17/30699

摘要： A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

摘要翻译： 基于协同过滤技术来确定用于多个项目的项目项目亲和度的集合。 确定基于项目项目亲和度的集合的项目的最近邻居项目的集合。 基于最小二乘回归确定用于多个项目和一组用户特征的一组用户特征项目亲和度。 部分基于用户特征项亲属度的集合来确定一组用户特征的最近邻居项目。 确定并存储每个项目和每个用户特征的最近邻项目的兼容关联权重。 基于特定用户的用户特征和特定用户消费的项目，包括用户的用户特征和用户消费的项目的最近邻项目的一组最近邻项目被识别为一组候选项，并且亲和度分数 确定候选项目。 至少部分地基于亲和度分数，向用户推荐来自该组候选项目的候选项目。

公开(公告)号：US20100250556A1

公开(公告)日：2010-09-30

申请号：US12416036

申请日：2009-03-31

申请人： Seung-Taek Park ,  Wei Chu ,  Todd Beaupre ,  Deepak K. Agarwal ,  Scott Roy ,  Raghu Ramakrishnan

发明人： Seung-Taek Park ,  Wei Chu ,  Todd Beaupre ,  Deepak K. Agarwal ,  Scott Roy ,  Raghu Ramakrishnan

IPC分类号： G06F17/30

CPC分类号： G06F17/30699

摘要： A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

摘要翻译： 基于协同过滤技术来确定用于多个项目的项目项目亲和度的集合。 确定基于项目项目亲和度的集合的项目的最近邻居项目的集合。 基于最小二乘回归确定用于多个项目和一组用户特征的一组用户特征项目亲和度。 部分基于用户特征项亲属度的集合来确定一组用户特征的最近邻居项目。 确定并存储每个项目和每个用户特征的最近邻项目的兼容关联权重。 基于特定用户的用户特征和特定用户消费的项目，包括用户的用户特征和用户消费的项目的最近邻项目的一组最近邻项目被识别为一组候选项，并且亲和度分数 确定候选项目。 至少部分地基于亲和度分数，向用户推荐来自该组候选项目的候选项目。

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。

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。

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.

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。

公开(公告)号：US20100241597A1

公开(公告)日：2010-09-23

申请号：US12407785

申请日：2009-03-19

申请人： Bee-Chung Chen ,  Pradheep Elango ,  Deepak K. Agarwal ,  Wei Chu

发明人： Bee-Chung Chen ,  Pradheep Elango ,  Deepak K. Agarwal ,  Wei Chu

IPC分类号： G06F15/18 ,  G06F15/16

CPC分类号： G06Q30/02 ,  G06F16/958

摘要： 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.

摘要翻译： 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。

公开(公告)号：US20100121801A1

公开(公告)日：2010-05-13

申请号：US12267538

申请日：2008-11-07

申请人： H. Scott Roy ,  Raghunath Ramakirshnan ,  Pardheep Elgano ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

发明人： H. Scott Roy ,  Raghunath Ramakirshnan ,  Pardheep Elgano ,  Nitin Motgi ,  Deepak K. Agarwal ,  Wei Chu ,  Bee-Chung Chen

IPC分类号： G06N5/02 ,  G06F17/00 ,  G06N7/00

CPC分类号： G06N7/005 ,  G06Q30/02 ,  H04L67/02

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。

公开(公告)号：US09600581B2

公开(公告)日：2017-03-21

申请号：US12388941

申请日：2009-02-19

申请人： Wei Chu ,  Seung-Taek Park

发明人： Wei Chu ,  Seung-Taek Park

IPC分类号： G06F17/30

CPC分类号： G06F17/30867

摘要： This disclosure describes systems and methods for selecting and/or ranking web-based content predicted to have the greatest interest to individual users. In particular, articles are ranked in terms of predicted interest for different users. This is done by optimizing an interest model and in particular through a method of bilinear regression and Bayesian optimization. The interest model is populated with data regarding users, the articles, and historical interest trends that types of users have expressed towards types of articles.