公开(公告)号：US09552555B1

公开(公告)日：2017-01-24

申请号：US14816866

申请日：2015-08-03

Applicant: Google Inc.

Inventor： Yangli Hector Yee ,  James Vincent McFadden ,  John Kraemer ,  Dasarathi Sampath

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06N99/005 ,  G06F17/3053 ,  G06F17/30554 ,  G06F17/30699 ,  G06F17/30781 ,  G06F17/30864 ,  G06N3/0472 ,  G06N7/005

Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.

Abstract translation: 提供了基于主题推荐内容的机制。 在一些实现中，提供了一种用于推荐内容项的方法，包括：确定与用户相关联的多个被访问的内容项，其中所述多个内容项中的每一个与多个主题相关联; 确定与所述多个所访问的内容项中的每一个相关联的所述多个主题; 基于所述多个主题生成用户兴趣的模型，其中所述模型实现机器学习技术以确定用于分配到所述多个主题中的每一个的多个权重; 对于多个内容项目，应用所述模型来确定所述用户将观看所述多个内容项目中的内容项目的概率; 基于所确定的概率对多个内容项进行排序; 以及基于所述排列的内容项目来选择所述多个内容项目的子集以推荐给所述用户。

公开(公告)号：US20170103343A1

公开(公告)日：2017-04-13

申请号：US15384692

申请日：2016-12-20

Applicant: Google Inc.

Inventor： Yangli Hector Yee ,  James Vincent McFadden ,  John Kraemer ,  Dasarathi Sampath

IPC: G06N99/00 ,  G06F17/30 ,  G06N7/00

CPC classification number: G06N20/00 ,  G06F16/24578 ,  G06F16/248 ,  G06F16/335 ,  G06F16/70 ,  G06F16/951 ,  G06N3/0472 ,  G06N7/005

Abstract: Mechanisms for recommending content items based on topics are provided. In some implementations, a method for recommending content items is provided that includes: determining a plurality of accessed content items associated with a user, wherein each of the plurality of content items is associated with a plurality of topics; determining the plurality of topics associated with each of the plurality of accessed content items; generating a model of user interests based on the plurality of topics, wherein the model implements a machine learning technique to determine a plurality of weights for assigning to each of the plurality of topics; applying the model to determine, for a plurality of content items, a probability that the user would watch a content item of the plurality of content items; ranking the plurality of content items based on the determined probabilities; and selecting a subset of the plurality of content items to recommend to the user based on the ranked content items.