Constrained models for optimized recommender systems

For recommender systems based on matrix factorization techniques the recommendation step scales linearly with the number of objects in the catalog. This leads to a serious bottleneck in large-scale applications that have a strict time budget and in which there may be millions of items. In this work it is developed a probabilistic model for the recommender system that exploiting some constraints allows to give high quality suggestions in sublinear time.