公开(公告)号：US20110087673A1

公开(公告)日：2011-04-14

申请号：US12577045

申请日：2009-10-09

申请人： Jiang Chen ,  Wei Chu ,  Zhenzhen Kou ,  Zhaohui Zheng

发明人： Jiang Chen ,  Wei Chu ,  Zhenzhen Kou ,  Zhaohui Zheng

IPC分类号： G06F17/30

CPC分类号： G06F17/30864

摘要： 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.

摘要翻译： 公开了与多个不同域的排序功能相关的方法和系统。 作为示例而非限制，可以基于域间丢失来训练针对多个不同域的排序功能，并且可以使用这样的排名功能来对来自多个不同域的搜索结果进行排名，使得它们可以在不规范相关性分数的情况下进行混合。

公开(公告)号：US10019518B2

公开(公告)日：2018-07-10

申请号：US12577045

申请日：2009-10-09

申请人： Jiang Chen ,  Wei Chu ,  Zhenzhen Kou ,  Zhaohui Zheng

发明人： Jiang Chen ,  Wei Chu ,  Zhenzhen Kou ,  Zhaohui Zheng

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06F16/951

摘要： 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.

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：US20120016642A1

公开(公告)日：2012-01-19

申请号：US12836188

申请日：2010-07-14

申请人： Lihong Li ,  Wei Chu ,  John Langford ,  Robert Schapire

发明人： Lihong Li ,  Wei Chu ,  John Langford ,  Robert Schapire

IPC分类号： G06F17/10 ,  G06F15/173

CPC分类号： G06Q30/0255 ,  G06Q30/02 ,  G06Q30/0269

摘要： Methods and apparatus for performing computer-implemented personalized recommendations are disclosed. User information pertaining to a plurality of features of a plurality of users may be obtained. In addition, item information pertaining to a plurality of features of the plurality of items may be obtained. A plurality of sets of coefficients of a linear model may be obtained based at least in part on the user information and/or the item information such that each of the plurality of sets of coefficients corresponds to a different one of a plurality of items, where each of the plurality of sets of coefficients includes a plurality of coefficients, each of the plurality of coefficients corresponding to one of the plurality of features. In addition, at least one of the plurality of coefficients may be shared among the plurality of sets of coefficients for the plurality of items. Each of a plurality of scores for a user may be calculated using the linear model based at least in part upon a corresponding one of the plurality of sets of coefficients associated with a corresponding one of the plurality of items, where each of the plurality of scores indicates a level of interest in a corresponding one of a plurality of items. A plurality of confidence intervals may be ascertained, each of the plurality of confidence intervals indicating a range representing a level of confidence in a corresponding one of the plurality of scores associated with a corresponding one of the plurality of items. One of the plurality of items for which a sum of a corresponding one of the plurality of scores and a corresponding one of the plurality of confidence intervals is highest may be recommended.

摘要翻译： 公开了用于执行计算机实现的个性化推荐的方法和装置。 可以获得与多个用户的多个特征有关的用户信息。 此外，可以获得与多个项目的多个特征有关的项目信息。 可以至少部分地基于用户信息和/或项目信息来获得线性模型的多组系数，使得多个系数集合中的每一个对应于多个项目中的不同项目，其中 所述多个系数集合中的每一个包括多个系数，所述多个系数中的每一个对应于所述多个特征中的一个。 此外，可以在多个项目的多个系数集合中共享多个系数中的至少一个。 可以使用线性模型来计算用户的多个评分中的每一个，至少部分地基于与多个项目中的相应一个项目相关联的多个系数集合中的对应的一组，其中多个分数中的每一个 表示多个项目中相应的一个项目的兴趣程度。 可以确定多个置信区间，所述多个置信区间中的每一个表示表示与所述多个项目中的对应的一个项目相关联的所述多个分数中的对应的一个分数中的置信水平的范围。 可以推荐多个评分中的相应一个分数和多个置信区间中的相应一个的最大值的多个项目中的一个。

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：US09519682B1

公开(公告)日：2016-12-13

申请号：US13117037

申请日：2011-05-26

申请人： Jay Pujara ,  Vishwanath Tumkur Ramarao ,  Xiaopeng Xi ,  Martin Zinkevich ,  Anirban Dasgupta ,  Belle Tseng ,  Wei Chu ,  Jyh-Shin Gareth Shue

发明人： Jay Pujara ,  Vishwanath Tumkur Ramarao ,  Xiaopeng Xi ,  Martin Zinkevich ,  Anirban Dasgupta ,  Belle Tseng ,  Wei Chu ,  Jyh-Shin Gareth Shue

IPC分类号： G06F11/00 ,  G06F17/30 ,  G06Q10/10 ,  H04H20/28 ,  H04H60/82 ,  G06F12/14

CPC分类号： G06F17/3053 ,  G06Q10/10 ,  H04H20/28 ,  H04H60/82

摘要： 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.

摘要翻译： 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。

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：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：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.