We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
While a user’s preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learni...
Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hon...
Probabilistic matrix factorization (PMF) is a powerful method for modeling data associated with pairwise relationships, finding use in collaborative filtering, computational biolo...
Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called R...
Ruslan Salakhutdinov, Andriy Mnih, Geoffrey E. Hin...