公开(公告)号：US20150242750A1

公开(公告)日：2015-08-27

申请号：US14188086

申请日：2014-02-24

Applicant: Google Inc.

Inventor： John Roberts Anderson ,  Ryan Michael Rifkin ,  Douglas Eck

IPC: G06N5/04

CPC classification number: G06Q30/0631 ,  G06F16/3347

Abstract: An asymmetric system for obtaining recommendations is disclosed. A reference magnitude may be obtained from a seed and/or a user model. The reference magnitude may be utilized to adjust the magnitude of candidate vectors that represent one or more items in a multi-dimensional vector space. This permits an item to receive credit for a popularity up to a certain point. The dot products between the adjusted candidate vectors and the seed vector may be obtained and, in some configurations, ranked. The highest dot products may correspond to items that are preferred to be recommended according to an implementation.

Abstract translation: 公开了一种用于获得建议的不对称系统。 可以从种子和/或用户模型获得参考幅度。 可以利用参考幅度来调整表示多维向量空间中的一个或多个项目的候选向量的大小。 这允许项目在一定程度上获得受欢迎程度的信用。 可以获得调整后的候选向量与种子向量之间的点积，并且在一些配置中可以进行排名。 最高点产品可以对应于根据实施方案推荐推荐的项目。

公开(公告)号：US10115146B1

公开(公告)日：2018-10-30

申请号：US14688691

申请日：2015-04-16

Applicant: GOOGLE INC.

Inventor： John Roberts Anderson ,  Ryan Michael Rifkin ,  Jay Yagnik ,  Rasmus Larsen ,  Sarvjeet Singh ,  Yi-Fan Chen ,  Anandsudhakar Kesari

IPC: G06Q30/00 ,  G06Q30/06 ,  G06Q30/02

Abstract: Implementations include systems and methods for scoring candidates for set recommendation problems. An example method includes repeating, for each code in code arrays for items in a set of items, determining a most common value for the code. In some implementations, the method includes determining that the most common value occurs with a frequency that meets an occurrence threshold and adding the code and the most common value to set-inclusion criteria. In other implementations, the method includes determining a value for the code from a code array for a seed item and adding the code and the most common value to set-inclusion criteria when the value for the code from the code array for the seed item matches the most common value. The method may also include evaluating a similarity with a candidate item based on the set-inclusion criteria and basing a recommendation regarding the candidate item on the similarity.

公开(公告)号：US20150170035A1

公开(公告)日：2015-06-18

申请号：US14096815

申请日：2013-12-04

Applicant: GOOGLE INC.

Inventor： Sarvjeet Singh ,  John Roberts Anderson ,  Ryan Michael Rifkin

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: A user model may be generated using affinity and exposure values for each item a user interacts with in an embedded space. The user model may include exemplars which may refer to representative items in the embedded space. Based on the user model, a recommendation of items may be provided to the user. A truncated form of the user model and/or the recommended items may be sent to the user's mobile device.

Abstract translation: 可以使用用户在嵌入式空间中交互的每个项目的亲和度和曝光值来生成用户模型。 用户模型可以包括可以参考嵌入空间中的代表性项目的示例。 基于用户模型，可以向用户提供项目的推荐。 可以将用户模型和/或推荐项目的截断形式发送到用户的移动设备。