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An improved constrained Bayesian probabilistic matrix factorization algorithm

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Abstract

Given the increasing growth of the Web and consequently the growth of e-commerce, the application of recommendation systems becomes more and more extensive. A good recommendation algorithm can provide a better user experience. In the collaborative filtering algorithm recommendation system, many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings, this paper proposes an improved constrained Bayesian probability matrix factorization algorithm. The algorithm introduces a potential similarity constraint matrix for specific sparsely scored users to affect the user’s feature vector, and uses the Logistic function to express the nonlinear relationship of the potential factors, combined with the Markov chain Monte Carlo method for training. Finally, the data set is used for testing and comparative evaluation. This experiment proves that the algorithmic model can be efficiently trained using Markov chain Monte Carlo methods by applying them to the MovieLens and Netflix dataset. The experimental results show that the algorithm has better predictive performance and is suitable for solving the problem of sparse rating matrix of specific users.

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Enquiries about data availability should be directed to the authors.

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Funding

The research content of this article is part of science and technology project of Jiangxi provincial department of China (2021204201400711). This research is mainly led by the big data application technology group, and the strategic partners are the intelligent network technology group.

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Authors and Affiliations

  1. School of Software Engineering, Jiangxi University of Science and Technology, Nanchang, China

    Guodong Wang, Musheng Chen, Junhua Wu, Mingzhe Fan & Qiuming Liu

Authors
  1. Guodong Wang
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  2. Musheng Chen
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  3. Junhua Wu
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  4. Mingzhe Fan
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  5. Qiuming Liu
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Correspondence to Guodong Wang.

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Wang, G., Chen, M., Wu, J. et al. An improved constrained Bayesian probabilistic matrix factorization algorithm. Soft Comput 27, 5751–5767 (2023). https://doi.org/10.1007/s00500-022-07799-x

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