记笔记

A Hybrid Collaborative Filtering Recommendation Algorithm Based on User Attributes and Matrix Completion

Jie YiMaosheng ZhongYinfen ChenAnquan Jie

School of Computer Information Engineering,Jiangxi Normal University

摘要：Collaborative filtering is a popular strategy in recommendation system.Traditional collaborative filtering relies on the user-item rating matrix that encodes the individual ratings of users for items to make recommendations.However,in the real-world,the rating matrix is highly sparse,and many new users do not have rating records,thus traditional collaborative filtering could not provide satisfactory recommendations.To alleviate this issue,we propose a hybrid algorithm that utilizes LMaFit to complete rating matrix,reducing the degree of sparsity,and provides a hybrid user-similarity to supply a good support for recommending to new users in the condition of cold start.Extensive experiment results on real-world datasets show the proposed algorithm has a better performance than other methods.

关键词：

Collaborative filtering; Data sparsity; Recommender system;

基金：

supported by National Natural Science Foundation of China（Grant No.61877031,61462027 and 61462045）； Science and Technology Project Founded by the Education Department of Jiangxi Province（Grant No.GJJ160277）；

会议名称：

2019 2nd International Conference on Communication,Network and Artificial Intelligence（CNAI 2019）

会议时间：

2019-12-27

会议地点：

中国广东广州