Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering

TL;DR

This paper introduces PACO, a Bayesian additive co-clustering model for joint collaborative filtering of ratings and reviews, providing interpretable recommendations and outperforming existing methods in prediction accuracy.

Contribution

The paper proposes a novel Bayesian technique for summing co-clusterings of Poisson distributions, enabling a simple, interpretable model for joint rating and review analysis.

Findings

Outperforms competitors in rating prediction accuracy.

Provides easily interpretable recommendations.

Uses a novel Bayesian summation of Poisson co-clusterings.

Abstract

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, since it is the latter where the reasoning for specific preferences is explicitly expressed. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret and give recommendations that are hard to clearly explain to users. In contrast, in this paper, we attack this problem through succinct additive co-clustering. We devise a novel Bayesian technique for summing co-clusterings of Poisson distributions. With this novel technique we propose a new Bayesian model for joint collaborative filtering of ratings and text reviews through a sum of simple co-clusterings. The simple structure of our model yields easily interpretable recommendations. Even with a simple, succinct structure, our model outperforms competitors in terms of predicting ratings with reviews.