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。