公开(公告)号：US20150051973A1

公开(公告)日：2015-02-19

申请号：US14468130

申请日：2014-08-25

Applicant: Yahoo! Inc.

Inventor： Lihong Li ,  Wei Chu ,  John Langford ,  Robert Schapire

IPC: G06Q30/02

CPC classification number: G06Q30/0255 ,  G06Q30/02 ,  G06Q30/0269

Abstract: 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.

Abstract translation: 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：US08909626B2

公开(公告)日：2014-12-09

申请号：US13660175

申请日：2012-10-25

Applicant: Yahoo! Inc.

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

IPC: G06F17/30

CPC classification number: G06F17/30699

Abstract: 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. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

Abstract translation: 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。 基于用户的用户特征和用户消费的项目，将一组最近邻项目识别为一组候选项目，并确定候选项目的亲和度分数。 基于亲和度分数，向用户推荐来自候选项集合的候选项目。

公开(公告)号：US20130054593A1

公开(公告)日：2013-02-28

申请号：US13660175

申请日：2012-10-25

Applicant: Yahoo! Inc.

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

IPC: G06F17/30

CPC classification number: G06F17/30699

Abstract: 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. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

Abstract translation: 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。 基于用户的用户特征和用户消费的项目，将一组最近邻项目识别为一组候选项目，并确定候选项目的亲和度分数。 基于亲和度分数，向用户推荐来自候选项集合的候选项目。