Predicting ratings in multi-criteria recommender systems via a collective factor model

In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute. Finding an effective method to exploit a user s multi-criteria ratings to predict the overall rating becomes one of the most important challenges. Among traditional solutions, most of the architectures are not designed in an end-to-end manner. These approaches initially estimate a user s multi-criteria scores, and train a separate model to predict the user s overall rating. This introduces extra training overhead, and the overall prediction accuracy is usually sensitive to its multi-criteria ratings models. In this paper, we propose a collective model to predict user s overall rating by automatically weighting each of the predicted multi-criteria sub-scores. The proposed architecture integrates the multi-criteria ratings and the overall rating models in a unified system, which allows to train and perform multi-criteria recommendation in an end-to-end manner. Experiments on 3 real datasets show that our proposed architectures achieve up to 13.14% lower prediction error over baseline approaches.