Open AccessMultiple similarity collaborative filtering recommendation among users
Author(s) -
Wenjing Yan,
Shuqing Li,
Cheng Yong-shang
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/768/7/072010
Subject(s) - collaborative filtering , similarity (geometry) , computer science , recommender system , quality (philosophy) , basis (linear algebra) , data mining , information retrieval , artificial intelligence , mathematics , philosophy , geometry , epistemology , image (mathematics)
[Objective] Through the analysis of multiple similarity among users, the problem that the traditional user based collaborative filtering algorithm only uses a single similarity and leads to the decline of recommendation quality is solved. [Method] The original single similarity calculation formula is improved, and the multiple similarity calculation formula is put forward, on this basis, the multiple similarity prediction score is calculated. [Result] By comparison with the traditional user based collaborative filtering algorithm, the method put forward in this paper has outstanding effect. [Limited] Users’ interests will change with time, so time information should be included in the calculation. [Conclusion] From the experiment, we can find that the improved method has better recommendation quality than traditional methods.