Open AccessA Hybrid Collaborative Filtering Recommendation Algorithm Based on User Attributes and Matrix Completion
Author(s) -
Jie Yi,
Maosheng Zhong,
Yinfen Chen,
Anquan Jie
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/790/1/012058
Subject(s) - collaborative filtering , recommender system , computer science , similarity (geometry) , matrix (chemical analysis) , sparse matrix , data mining , cold start (automotive) , artificial intelligence , machine learning , algorithm , information retrieval , engineering , materials science , physics , composite material , quantum mechanics , gaussian , image (mathematics) , aerospace engineering
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.