公开(公告)号：US09858308B2

公开(公告)日：2018-01-02

申请号：US14599026

申请日：2015-01-16

Applicant: Google Inc.

Inventor： Xiaohong Gong ,  Wei Zhang ,  Nima Asgharbeygi

IPC: G06F7/00 ,  G06F17/30 ,  H04L29/08 ,  G06Q30/06 ,  H04N21/466

CPC classification number: G06F17/30345 ,  G06F17/30386 ,  G06F17/30867 ,  G06Q30/0631 ,  H04L67/30 ,  H04L67/306 ,  H04N21/4668

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

公开(公告)号：US20160210321A1

公开(公告)日：2016-07-21

申请号：US14599026

申请日：2015-01-16

Applicant: Google Inc.

Inventor： Xiaohong Gong ,  Wei Zhang ,  Nima Asgharbeygi

IPC: G06F17/30 ,  H04L29/08

CPC classification number: G06F17/30345 ,  G06F17/30386 ,  G06F17/30867 ,  G06Q30/0631 ,  H04L67/30 ,  H04L67/306 ,  H04N21/4668

Abstract: System and methods of this disclosure are directed to recommending content in real-time or near real-time. The system comprises a number of pipelines updated a different frequencies that process temporally different sets of web property visit data. Within each pipeline, the system can employ different number of algorithms to process visit data to generate content recommendations. One algorithm is a content filter that filters from the visit data those determined to be unsuitable as recommendations. Another is a content analyzer that analyzes the content of each URL in the visit data by topic category and attribute. Another is an item-to-item collaborative filter that determines a correlation score for each URL based on those in the visit data in a single session Another is a device-to-item matrix factorization that determines an affinity score for each URL based on visit data, context information, and topic category.

Abstract translation: 本公开的系统和方法旨在实时或接近实时地推荐内容。 该系统包括多个管道更新处理时间上不同的web属性访问数据集合的不同频率。 在每个流水线中，系统可以使用不同数量的算法来处理访问数据以生成内容建议。 一种算法是从确定为不适合作为推荐的访问数据过滤的内容过滤器。 另一个是内容分析器，按照主题类别和属性分析访问数据中每个URL的内容。 另一个是项目到项目协作过滤器，其基于单个会话中的访问数据中的那些确定每个URL的相关性得分另一个是设备到项目矩阵因式分解，其基于访问确定每个URL的亲和度分数 数据，上下文信息和主题类别。

公开(公告)号：US20150363859A1

公开(公告)日：2015-12-17

申请号：US14162266

申请日：2014-01-23

Applicant: Google Inc.

Inventor： Wei Zhang ,  Jia Zhao ,  Duangmanee Putthividhya ,  Ismail Oner Sebe ,  Nima Asgharbeygi

IPC: G06Q30/06

CPC classification number: G06Q30/0631

Abstract: Systems and methods for determining correlation scores for product pairs are provided. Contextual user behavior indicator data relating to a plurality of user behavior indicator types is received. A correlation score is computed for a first product and a second product for each user behavior indicator type from the plurality of user behavior indicator types. A final correlation score is computed for the first product and the second product by combining the computed correlation scores for each user behavior indicator type. The computed final correlation score for the first product and the second product is stored into a first data storage.

Abstract translation: 提供了用于确定产品对的相关性得分的系统和方法。 接收与多个用户行为指示符类型相关的上下文用户行为指示符数据。 针对来自多个用户行为指示符类型的每个用户行为指示符类型的第一乘积和第二乘积计算相关得分。 通过组合每个用户行为指示符类型的计算的相关性分数来计算第一乘积和第二乘积的最终相关分数。 所计算的第一产品和第二产品的最终相关性得分被存储在第一数据存储器中。