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.
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
- 专辑:
信息科技
- 专题:
计算机软件及计算机应用
- DOI:
10.26914/c.cnkihy.2019.057601
- 分类号:
TP391.3
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