Personalized Commodity Recommendations of Retail Business using User Feature based Collaborative Filtering

被引：4

作者：

Wang, Feiran ^{[1
,2
]}

Wen, Yiping ^{[1
]}

Guo, Tianhang ^{[1
]}

Chen, Jinjun ^{[1
,2
]}

Cao, Buqing ^{[1
]}

机构：

[1] Hunan Univ Sci & Technol, Key Lab Knowledge Proc & Networked Manufacture, Xiangtan, Peoples R China

[2] Swinburne Univ Technol, Swinburne Data Sci Res Inst, Hawthorn, Vic, Australia

来源：

2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS | 2018年

关键词：

recommendation; collaborative filtering; user feature;

D O I：

10.1109/BDCloud.2018.00051

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Collaborative filtering is an extensively adopted approach for commodity recommendation. This paper proposes a user feature based collaborative filtering algorithm named UFCF for personalized commodity recommendations of retail business. It adopts matrix factorization and user features that are extracted from users' behaviors to improve the accuracy of recommendation result and alleviate the impact of sparse data. Experiments with real datasets from a supermarket marketing group demonstrate the effectiveness of the algorithm.

引用

页码：273 / 278

页数：6

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