research-article

Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

Authors:

Majjed Al-QatfAuthors Info & Claims

Applied Intelligence, Volume 51, Issue 7

Pages 5132 - 5145

https://doi.org/10.1007/s10489-020-02049-9

Published: 01 July 2021 Publication History

Abstract

Probabilistic matrix factorization (PMF) is the most popular method among low-rank matrix approximation approaches that address the sparsity problem in collaborative filtering for recommender systems. PMF depends on the classical maximum a posteriori estimator for estimating model parameters; however, these approaches are vulnerable to overfitting because of the nature of a single point estimation that is pursued by these models. An alternative approach to PMF is a Bayesian PMF model that suggests the Markov chain Monte Carlo algorithm as a full estimation for approximate intractable posterior over model parameters. However, despite its success in increasing prediction, it has a high computational cost. To this end, we proposed a novel Bayesian deep learning-based model treatment, namely, variational autoencoder Bayesian matrix factorization (VABMF). The proposed model uses stochastic gradient variational Bayes to estimate intractable posteriors and expectation–maximization-style estimators to learn model parameters. The model was evaluated on the basis of three MovieLens datasets, namely, Ml-100k, Ml-1M, and Ml-10M. Experimental results showed that our proposed VABMF model significantly outperforms state-of-the-art RS.

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Cited By

Li MWang HJimack P(2024)Generative Modeling of Sparse Approximate Inverse PreconditionersComputational Science – ICCS 202410.1007/978-3-031-63759-9_40(378-392)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-63759-9_40
Yadav GAbdel-Nasser MRashwan HPuig DNandi G(2023)Implicit regularization of a deep augmented neural network model for human motion predictionApplied Intelligence10.1007/s10489-022-04419-x53:14(18027-18040)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-04419-x
Fraihat SAbu Tahon BAlhijawi BAwajan A(2023)Deep encoder–decoder-based shared learning for multi-criteria recommendation systemsNeural Computing and Applications10.1007/s00521-023-09007-935:34(24347-24356)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00521-023-09007-9

Index Terms

Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 51, Issue 7

Jul 2021

1092 pages

ISSN:0924-669X

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© Springer Science+Business Media, LLC, part of Springer Nature 2021.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2021

Accepted: 28 October 2020

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National Natural Science Foundation of China

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Cited By

Li MWang HJimack P(2024)Generative Modeling of Sparse Approximate Inverse PreconditionersComputational Science – ICCS 202410.1007/978-3-031-63759-9_40(378-392)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/978-3-031-63759-9_40
Yadav GAbdel-Nasser MRashwan HPuig DNandi G(2023)Implicit regularization of a deep augmented neural network model for human motion predictionApplied Intelligence10.1007/s10489-022-04419-x53:14(18027-18040)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1007/s10489-022-04419-x
Fraihat SAbu Tahon BAlhijawi BAwajan A(2023)Deep encoder–decoder-based shared learning for multi-criteria recommendation systemsNeural Computing and Applications10.1007/s00521-023-09007-935:34(24347-24356)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s00521-023-09007-9

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