Abstract
Matrix factorization is a popular collaborative filtering technique, assuming that the matrix of ratings can be written as the inner product of two low-rank matrices, comprising latent features assigned to each user/item. Recently, several researchers have developed Bayesian treatments of matrix factorization, that infer posterior distributions over the postulated user and item latent features. As it has been shown, by allowing for taking uncertainty into account, such Bayesian inference approaches can better model sparse data, which are prevalent in real-world applications. In this paper, we consider replacing the inner product in the likelihood function of Bayesian matrix factorization with an arbitrary function that we learn from the data at the same time as we learn the latent feature posteriors; specifically, we parameterize the likelihood function using dense layer (DL) deep networks. In addition, to allow for addressing the cold-start problem, we also devise a model extension that takes into account item content, treated as side information. We provide extensive experimental evaluations on several real-world datasets; we show that our method completely outperforms state-of-the-art alternatives, without compromising computational efficiency.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I.J., Bergeron, A., Bouchard, N., Bengio, Y.: Theano: new features and speed improvements. In: Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop (2012)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12, 2121–2159 (2010)
Jaakkola, T., Jordan, M.: Bayesian parameter estimation via variational methods. Stat. Comput. 10, 25–37 (2000)
Kingma, D., Welling, M.: Auto-encoding variational Bayes. In: Proceedings of the ICLR (2014)
Kingma, D.P., Rezende, D.J., Mohamed, S., Welling, M.: Semi-supervised learning with deep generative models. In: Proceedings of the NIPS (2014)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the ACM SIGKDD, pp. 426–434 (2008)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 512, 436–444 (2015)
McFee, B., Bertin-Mahieux, T., Ellis, D.P.W., Lanckriet, G.R.G.: The million song dataset challenge. In: Proceedings of the WWW, pp. 909–916 (2012)
Nair, V., Hinton, G.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the ICML (2010)
Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proceedings of the ICDM, pp. 502–511 (2008)
Park, S., Kim, Y.D., Choi, S.: Hierarchical Bayesian matrix factorization with side information. In: Proceedings of the IJCAI (2013)
Porteous, I., Asuncion, A., Welling, M.: Bayesian matrix factorization with side information and Dirichlet process mixtures. In: Proceedings of the AAAI (2010)
Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: Proceedings of the ICML (2014)
Rezende, D.J., Mohamed, S.: Variational inference with normalizing flows. In: Proceedings of the ICML (2015)
Salakhutdinov, R., Murray, I.: On the quantitative analysis of deep belief networks. In: Proceedings of the ICML, pp. 872–879. ACM Press (2008)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of the NIPS (2007)
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of ICML (2008)
Shi, J., Wang, N., Xia, Y., Yeung, D.Y., King, I., Jia, J.: SCMF: sparse covariance matrix factorization for collaborative filtering. In: Proceedings of the IJCAI (2013)
Wang, X., Wang, Y.: Improving content-based and hybrid music recommendation using deep learning. In: Proceedings of the ACM MultiMedia (2014)
Acknowledgment
This work has been partially supported by the NVIDIA Corporation, as well as the EU H2020 NOTRE project (grant 692058).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Chatzis, S.P. (2017). Deep Bayesian Matrix Factorization. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-57529-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57528-5
Online ISBN: 978-3-319-57529-2
eBook Packages: Computer ScienceComputer Science (R0)