Reviews: Markov Random Fields for Collaborative Filtering

The paper presents a novel method for recommendation with collaborative filtering based on Markov Random Fields (MRF). Starting from a general approach that regresses the full graph of items, the paper shows that a valid approximation can be obtained by proceeding with subgraphs that represent Markov blankets of an initial set of items. This approach yields significant computing gains, while yielding better recommendation performance compared to the state-of-the-art represented here by variational auto-encoders. As a general comment, I am wondering whether taking into account the popularity bias makes sense in the approach and if the authors thought about it. The claims are well supported by theoretical analysis.