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

Lin Li

doi:10.1117/12.2626526

21 December 2021 Matrix decomposition model of E-commerce based on collaborative filtering algorithm

Lin Li

Proceedings Volume 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021); 121560Y (2021) https://doi.org/10.1117/12.2626526
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 2021, Sanya, China

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.

Citation Download Citation

Lin Li "Matrix decomposition model of E-commerce based on collaborative filtering algorithm", Proc. SPIE 12156, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2021), 121560Y (21 December 2021); https://doi.org/10.1117/12.2626526

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