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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.
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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