Bayesian probabilistic tensor factorization for recommendation and rating aggregation with multicriteria evaluation data

Abstract Ratings by users on various items such as products and services have become easily available on the Web. Also available in many cases, in addition to an overall rating for each item by each user, are multicriteria ratings from different viewpoints. Our previous study showed that multicriteria rating approaches performed better than single-criterion ones for both recommendation and rating aggregation. We have now formulated a Bayesian probabilistic model for multicriteria evaluation as an alternative to low-rank approximation. We evaluated the performance of this model, in which model capacity is controlled by integrating over all model parameters, and investigated whether it can be made to work more efficiently by using a Markov chain Monte Carlo method for both recommendation and rating aggregation. It performed better than low-rank approximation methods that obtain a maximum a posteriori estimate by fitting to the data.