Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering

Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret. In this paper, we attack this problem through succinct additive co-clustering on both ratings and reviews. Our model yields accurate and interpretable recommendations.