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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| Vydáno v: | Applied intelligence (Dordrecht, Netherlands) Ročník 51; číslo 7; s. 5132 - 5145 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Springer US
01.07.2021
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| 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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| AbstractList | 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. |
| Author | Mohsen, Farida Al-Qatf, Majjed Aldhubri, Ali Lasheng, Yu |
| Author_xml | – sequence: 1 givenname: Ali orcidid: 0000-0002-2926-4240 surname: Aldhubri fullname: Aldhubri, Ali organization: School of Computer Science and Engineering, Central South University – sequence: 2 givenname: Yu orcidid: 0000-0001-7078-9068 surname: Lasheng fullname: Lasheng, Yu email: yulasheng@csu.edu.cn organization: School of Computer Science and Engineering, Central South University – sequence: 3 givenname: Farida orcidid: 0000-0002-0766-4315 surname: Mohsen fullname: Mohsen, Farida organization: School of Computer Science and Engineering, Central South University – sequence: 4 givenname: Majjed orcidid: 0000-0002-1796-344X surname: Al-Qatf fullname: Al-Qatf, Majjed organization: School of Computer Science and Technology, University of Science and Technology of China |
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| CitedBy_id | crossref_primary_10_1109_TIM_2023_3324674 crossref_primary_10_1111_coin_70062 crossref_primary_10_1007_s10489_025_06301_y crossref_primary_10_1007_s10489_022_04419_x crossref_primary_10_1007_s00521_023_09007_9 crossref_primary_10_1109_ACCESS_2025_3583186 |
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| Keywords | Recommender system (RS) Collaborative filtering (CF) Variational autoencoder (VAE) Variational autoencoder Bayesian matrix factorization (VABMF) |
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| Title | Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering |
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