Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
ECML '07 Proceedings of the 18th European conference on Machine Learning
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Bayesian matrix co-factorization: variational algorithm and Cramér-Rao bound
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
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Bayesian treatment of matrix factorization has been successfully applied to the problem of collaborative prediction, where unknown ratings are determined by the predictive distribution, inferring posterior distributions over user and item factor matrices that are used to approximate the user-item matrix as their product. In practice, however, Bayesian matrix factorization suffers from cold-start problems, where inferences are required for users or items about which a sufficient number of ratings are not gathered. In this paper we present a method for Bayesian matrix factorization with side information, to handle cold-start problems. To this end, we place Gaussian-Wishart priors on mean vectors and precision matrices of Gaussian user and item factor matrices, such that mean of each prior distribution is regressed on corresponding side information. We develop variational inference algorithms to approximately compute posterior distributions over user and item factor matrices. In addition, we provide Bayesian Cramér-Rao Bound for our model, showing that the hierarchical Bayesian matrix factorization with side information improves the reconstruction over the standard Bayesian matrix factorization where the side information is not used. Experiments on MovieLens data demonstrate the useful behavior of our model in the case of cold-start problems.