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

arXiv: https://arxiv.org/abs/1512.01845 · HTML: https://mlsanity.com/paper/1512.01845/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2015.

## Built on by (most cited first, 5 of 5)

- [Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model](https://mlsanity.com/paper/2109.02058/) (2021)
- [Representation learning in multiplex graphs: Where and how to fuse information?](https://mlsanity.com/paper/2402.17906/) (2024)
- [GSim: A Graph Neural Network based Relevance Measure for Heterogeneous Graphs](https://mlsanity.com/paper/2208.06144/) (2022)
- [Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems](https://mlsanity.com/paper/2604.18351/) (2026)
- [Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark](https://mlsanity.com/paper/2606.12581/) (2026)

## Abstract (arXiv)

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.