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Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering

Author

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  • Liu, Jie
  • Ye, Zifeng
  • Chen, Kun
  • Zhang, Panpan

Abstract

A network-based method applied to collaborative filtering in recommender systems is introduced in this paper. Specifically, a novel mixed-membership stochastic block model with a conjugate prior from the exponential family is proposed for bipartite networks. The analytical expression of the model is derived, and a variational Bayesian algorithm that is computationally feasible for approximating the untractable posterior distributions is presented. Extensive simulations show that the proposed model provides more accurate inference than competing methods with the presence of outliers. The proposed model is also applied to a MovieLens dataset for a real data application.

Suggested Citation

  • Liu, Jie & Ye, Zifeng & Chen, Kun & Zhang, Panpan, 2024. "Variational Bayesian inference for bipartite mixed-membership stochastic block model with applications to collaborative filtering," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:csdana:v:189:y:2024:i:c:s0167947323001470
    DOI: 10.1016/j.csda.2023.107836
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Roger Guimerà & Alejandro Llorente & Esteban Moro & Marta Sales-Pardo, 2012. "Predicting Human Preferences Using the Block Structure of Complex Social Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    3. David T. Frazier & Ruben Loaiza-Maya & Gael M. Martin, 2021. "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Papers 2106.12262, arXiv.org, revised Feb 2022.
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