公开(公告)号：US20150339381A1

公开(公告)日：2015-11-26

申请号：US14284647

申请日：2014-05-22

Applicant: Yahoo!, Inc.

Inventor： Vidit Jain ,  Abhranil Chatterjee

IPC: G06F17/30

CPC classification number: G06F17/30958

Abstract: Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.

Abstract translation: 用户可以从各种来源（例如通过网站访问的视频）消费各种各样的内容。 如本文所提供的，可以识别与用户所消费的当前内容上下文和/或语义相关的内容推荐，并将其提供给用户。 例如，可以识别由用户观看的视频的元数据（例如，从描述提取的术语，用户评论，类别和/或其他信息）。 元数据可以用于基于对应于分组为包括从内容语料库提取的术语和关系之间的关系的图的精简主题分组的集合的元数据来标识内容建议。 元数据可以与精简主题分组集合内的相关项匹配，并且可以向用户建议包括对应于相关术语的内容的内容建议。

公开(公告)号：US09959364B2

公开(公告)日：2018-05-01

申请号：US14284647

申请日：2014-05-22

Applicant: Yahoo!, Inc.

Inventor： Vidit Jain ,  Abhranil Chatterjee

IPC: G06F17/30

CPC classification number: G06F17/30958

Abstract: Users consume a wide variety of content from various sources, such as videos accessible through websites. As provided herein, content recommendations that are contextually and/or semantically relevant to current content consumed by a user may be identified and provided to the user. For example, metadata for a video being watched by the user may be identified (e.g., terms extracted from a description, user reviews, a category, and/or other information). The metadata may be used to identify content recommendations based upon the metadata corresponding to terms grouped into a set of refined topic groupings of a graph comprising terms and relationships between terms extracted from a content corpus. The metadata may be matched to relevant terms within the set of refined topic groupings, and content recommendations comprising content corresponding to the relevant terms may be suggested to the user.