# Ordinal Graph Gamma Belief Network for Social Recommender Systems

arXiv: https://arxiv.org/abs/2209.05106 · HTML: https://mlsanity.com/paper/2209.05106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 12 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Graph Neural Networks for Social Recommendation](https://mlsanity.com/paper/1902.07243/) (2019)
- [A Neural Influence Diffusion Model for Social Recommendation](https://mlsanity.com/paper/1904.10322/) (2019)
- [Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction](https://mlsanity.com/paper/1501.06218/) (2015)
- [The Poisson Gamma Belief Network](https://mlsanity.com/paper/1511.02199/) (2015)
- [Ordinal Non-negative Matrix Factorization for Recommendation](https://mlsanity.com/paper/2006.01034/) (2020)
- [Nonparametric Bayesian Factor Analysis for Dynamic Count Matrices](https://mlsanity.com/paper/1512.08996/) (2015)
- [Nonparametric Bayesian Negative Binomial Factor Analysis](https://mlsanity.com/paper/1604.07464/) (2016)
- [Recommendation from Raw Data with Adaptive Compound Poisson Factorization](https://mlsanity.com/paper/1905.13128/) (2019)

## Abstract (arXiv)

To build recommender systems that not only consider user-item interactions represented as ordinal variables, but also exploit the social network describing the relationships between the users, we develop a hierarchical Bayesian model termed ordinal graph factor analysis (OGFA), which jointly models user-item and user-user interactions. OGFA not only achieves good recommendation performance, but also extracts interpretable latent factors corresponding to representative user preferences. We further extend OGFA to ordinal graph gamma belief network, which is a multi-stochastic-layer deep probabilistic model that captures the user preferences and social communities at multiple semantic levels. For efficient inference, we develop a parallel hybrid Gibbs-EM algorithm, which exploits the sparsity of the graphs and is scalable to large datasets. Our experimental results show that the proposed models not only outperform recent baselines on recommendation datasets with explicit or implicit feedback, but also provide interpretable latent representations.