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：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：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.

公开(公告)号：US08442929B2

公开(公告)日：2013-05-14

申请号：US12613503

申请日：2009-11-05

申请人： Seung-Taek Park ,  Wei Chu ,  Wei Du ,  Uminder Singh ,  Jessi Dong

发明人： Seung-Taek Park ,  Wei Chu ,  Wei Du ,  Uminder Singh ,  Jessi Dong

IPC分类号： G06F17/00 ,  G06N5/00

CPC分类号： G06Q10/04

摘要： Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

摘要翻译： 确定两个项目与每个项目类似，不仅基于以前的实际用户行为，而且还基于所观察到的这两个项目的特征的相关性。 如果选择具有第一特征的项目的高比例的用户也选择具有第二特征的项目，则第一特征和第二特征被确定为彼此具有一些亲和力，反之亦然。 具有彼此具有高亲和力特征的两个项目被确定为彼此具有一些相似性，即使选择了这些项目中的一个的用户很少或没有选择其中一个项目。 可以向已经选择第二项目的用户潜在地也对该用户感兴趣的用户推荐被确定为以这种方式与第二项目充分相似的第一项目。

公开(公告)号：US20110112981A1

公开(公告)日：2011-05-12

申请号：US12615058

申请日：2009-11-09

申请人： Seung-Taek Park ,  Wei Chu

发明人： Seung-Taek Park ,  Wei Chu

IPC分类号： G06Q30/00 ,  G06F17/30

CPC分类号： G06Q30/02 ,  G06F16/9535 ,  G06Q30/0282

摘要： A method and a system are provided for recommending an ad (e.g., item) for a user. In one example, the system constructs one or more user profiles. Each user profile is represented by a user feature set including user attributes. The system constructs one or more item profiles. Each item profile is represented by an item feature set including item attributes. The system receives historical item ratings given by one or more users. The system then generates one or more preference scores by modeling at least one relationship among the user profiles, the item profiles and the historical item ratings.

摘要翻译： 提供了一种用于为用户推荐广告（例如，项目）的方法和系统。 在一个示例中，系统构造一个或多个用户简档。 每个用户简档由包括用户属性的用户特征集表示。 系统构造一个或多个项目简档。 每个项目简档由包括项目属性的项目功能集表示。 系统接收一个或多个用户给出的历史项目评级。 然后，系统通过对用户简档，项目简档和历史项目评级中的至少一个关系进行建模来生成一个或多个偏好分数。

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：US20110107260A1

公开(公告)日：2011-05-05

申请号：US12613503

申请日：2009-11-05

申请人： Seung-Taek Park ,  Wei Chu ,  Wei Du ,  Uminder Singh ,  Jessi Dong

发明人： Seung-Taek Park ,  Wei Chu ,  Wei Du ,  Uminder Singh ,  Jessi Dong

IPC分类号： G06F3/048

CPC分类号： G06Q10/04

摘要： Two items are determined to be similar to each not only based on previous actual user behavior, but also based on the observed relatedness of the characteristics of those two items. A first characteristic and a second characteristic are determined to have some affinity for each other if a high proportion of users who select items having the first characteristics also select items that have the second characteristic, and vice-versa. Two items having characteristics with high affinity for each other are determined to have some similarity to each other, even if very few or no users who selected one of those items ever selected the other of those items. A first item that is determined to be sufficiently similar to second item in this manner may be recommended to a user who has selected the second item as potentially also being of interest to that user.

摘要翻译： 确定两个项目与每个项目类似，不仅基于以前的实际用户行为，而且还基于所观察到的这两个项目的特征的相关性。 如果选择具有第一特征的项目的高比例的用户也选择具有第二特征的项目，则第一特征和第二特征被确定为彼此具有一些亲和力，反之亦然。 具有彼此具有高亲和力特征的两个项目被确定为彼此具有一些相似性，即使选择了这些项目中的一个的用户很少或没有选择其中一个项目。 可以向已经选择第二项目的用户潜在地也对该用户感兴趣的用户推荐被确定为以这种方式与第二项目充分相似的第一项目。

公开(公告)号：US20100211568A1

公开(公告)日：2010-08-19

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.

摘要翻译： 本公开描述了用于选择和/或排名被预测为对个体用户最感兴趣的基于网络的内容的系统和方法。 特别地，文章按不同用户的预期兴趣排列。 这通过优化兴趣模型，特别是通过双线性回归和贝叶斯优化的方法来完成。 兴趣模型填充有关用户，文章和用户对文章类型表达的历史兴趣趋势的数据。

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：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。