Recommender Systems on Factor Graphs

Recommender systems suggest to consumers items that meet their interests and preferences, which frees users from the overwhelming amount of available information. However, there are certain challenges to design scalable, accurate and dependable recommender systems, as the available data for the recommender systems is incomplete, uncertain, inconsistent and/or intentionally contaminated. Most existing popular Matrix Factorization recommender algorithms are shown to be prone to malicious behaviors and they have scalability issues. We formulate the recommendation problem as an inference problem and aim to compute the marginal probability distributions of the variables which represent the ratings of items to be predicted. By modeling the recommender system on a factor graph, we then utilize the Belief Propagation algorithm to efficiently calculate the marginal probability distributions through message-passing on the factor graph. The BP-based recommender system (BPRS) is comparable to the state of art methods such as Correlation-based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy. Moreover, as opposed to the previous recommender algorithms, BPRS does not require solving the recommendation problem for all the users if it wishes to update the recommendations for only a single active user. Therefore, BP-based recommendation algorithm is a new promising approach which offers a significant advantage on scalability while providing competitive accuracy for the recommender systems.