A novel method for matrix factorization in recommender system using item's information

被引：0

作者：

Zhao, Jianli ^{[1
]}

Wu, Wenmin ^{[1
]}

Zhang, Chunsheng ^{[1
]}

Meng, Fang ^{[1
]}

机构：

[1] School of Information Science and Engineering, Shandong University of Science and Technology, Qingdao

来源：

Journal of Computational Information Systems | 2015年 / 11卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Collaborative filtering; Fuzzy set; Matrix factorization; Recommender systems;

D O I：

10.12733/jcis14063

中图分类号：

学科分类号：

摘要：

Collaborative Filtering has been thoroughly investigated these years and one of the most popular approaches to it is matrix factorization. Traditional matrix factorization models only rely on rating data. In this paper, we take the content of items into account to improve the matrix factorization. We proposed a novel action-content cluster method with fuzzy logic, and calculate user's similarities based on user's different memberships to different groups (fuzzy set). We use user's similarities to optimize the matrix factorization during the learning process. Experiments with Movielens datasets show that our proposed model significantly outperforms the baseline and original model. ©, 2015, Binary Information Press. All right reserved.

引用

页码：3517 / 3524

页数：7

共 22 条

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