Open AccessImproved Metric Factorization Recommendation Algorithm Based on Social Networks and Implicit Feedback
Author(s) -
Bilin Wang,
Jiaxin Han,
Ying Cuan
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1634/1/012037
Subject(s) - metric (unit) , factorization , computer science , matrix decomposition , ranking (information retrieval) , algorithm , recommender system , artificial intelligence , machine learning , eigenvalues and eigenvectors , operations management , physics , quantum mechanics , economics
The Metric Factorization algorithm solves the problem of the suboptimal solution caused by the inner product of the traditional matrix factorization algorithm. Although the basic metric factorization model has achieved good results in rating prediction and item ranking tasks, the algorithm ignores the role of implicit feedback and user social information. Considering the social relationship and implicit feedback information between users, this paper improves the basic metric factor Factorization algorithm, and proposes an improved metric factorization recommendation algorithm based on social networks and implicit feedback. We do rating prediction tasks on the Filmtrust and Last.FM datasets, experimental results show that the improved algorithm can further improve the accuracy of prediction.