Matrix decomposition model of E-commerce based on collaborative filtering algorithm

Abstract

Currently, there is a distinct contradiction between the massive information and the lack of personalized information. In this case, the recommendation system has been widely used as an important technology to discover the potential interests of users and recommend the items of interest to the target users. Considering that the probability matrix decomposition model can show its prediction mechanism more clearly, in this paper, the probabilistic matrix factorization model is used in the matrix factorization method. Based on the probabilistic matrix factorization, a recommendation method framework considering both extreme rating behavior similarity and rating matrix information fusion is proposed. The framework integrates the local neighbor relationship of users into the global rating optimization process of matrix factorization, and thus improves the prediction accuracy and robustness of sparse data. Simulation results show that the proposed method reduces the MAE by 0.68%, 1.12%, 2.85% and 1.19% compared with the suboptimal method. It is proved that the proposed method can effectively implement long tail project recommendation and ensure high recommendation accuracy.