Probabilistic matrix factorization from quantized measurements

Giulio Bottegal, Johan A. K. Suykens
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
We consider the problem of factorizing a matrix with discrete-valued entries as a product of two low-rank matrices. Under a probabilistic framework, we seek for the minimum mean-square error estimates of these matrices, using full Bayes and empirical Bayes approaches. In the first case, we devise an integration scheme based on the Gibbs sampler that accounts also for hyperparameter and noise variance estimation. A similar technique is used also for the latter case, where we combine Gibbs
more » ... g with the expectation-maximization (EM) algorithm to estimate the model parameters via marginal likelihood maximization. Extension to the case of missing values is also discussed. The proposed methods are evaluated on simulated data, and on a real data set for recommender systems.
doi:10.1109/ijcnn.2017.7965865 dblp:conf/ijcnn/BottegalS17 fatcat:fm4jd57gtfbepnnvbd2wncnmye