Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

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; howeve...

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Veröffentlicht in:Applied intelligence (Dordrecht, Netherlands) Jg. 51; H. 7; S. 5132 - 5145
Hauptverfasser: Aldhubri, Ali, Lasheng, Yu, Mohsen, Farida, Al-Qatf, Majjed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.07.2021
Springer Nature B.V
Schlagworte:
Algorithms
Approximation
Artificial Intelligence
Bayesian analysis
Collaboration
Computer Science
Factorization
Filtration
Machine learning
Machines
Manufacturing
Markov chains
Mathematical models
Mechanical Engineering
Parameter estimation
Processes
Recommender systems
Recommender system (RS)
Collaborative filtering (CF)
Variational autoencoder (VAE)
Variational autoencoder Bayesian matrix factorization (VABMF)
ISSN:0924-669X, 1573-7497
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Zusammenfassung: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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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-02049-9