Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

Ali Aldhubri

doi:10.1007/s10489-020-02049-9

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Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

2021-01-07

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DOI：10.1007/s10489-020-02049-9

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Abstract

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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.

Keywords:

Recommender system (RS)

Collaborative filtering (CF)

Variational autoencoder (VAE)

Variational autoencoder Bayesian matrix factorization (VABMF)