Recommendation Algorithm Optimization Based on Matrix Factorization

Abstract

In the paper, the influence of user trust relationship on recommendation is mainly analyzed, user rating and trust relationship are also clearly defined. Meanwhile, the traditional SVD matrix factorization algorithm is extended to introduce the explicit influence (user trust value) and the implicit influence (friends trusted by users) of the user trust into the matrix factorization recommendation model. Subsequently, TFMF (Trust-fused Matrix Factorization) recommendation model is proposed in the paper to infer model training & learning in detail. The user trust relationship is not considered in implicit feedback SVD++, in other words, the itemratings marked by the trusted users can also influence the recommendation prediction. Therefore, the matrix factorization recommendation algorithm based on implicit trust relationship fusion is designed in the paper. Compared with original matrix factorization algorithm, the proposed algorithm not only has better prediction accuracy and flexibility, but also has better performance in the sparse data set due to the introduction of the trust relationship implicit feedback mechanism.