Abstract
Collaborative filtering (CF) in the recommendation system using user habits, behaviors, and item rating to recommend the products which suit customer’s needs. Therefore, analyzing user rating data is one of the factors that improve the efficiency of the recommendation system. This paper proposes a new approach to analyze rating item and input the implicit effect of items rating to the recommendation system based on the TrustSVD model and matrix factorization (MF) techniques. The experimental results showed that our proposed solution achieves 18% better than the matrix factorization method and 15% the Multi-Relational Matrix Factorization method, respectively.
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