# Scalable Recommendation with Poisson Factorization

arXiv: https://arxiv.org/abs/1311.1704 · HTML: https://mlsanity.com/paper/1311.1704/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2013.

## Built on by (most cited first, 8 of 8)

- [Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation](https://mlsanity.com/paper/2209.11679/) (2022)
- [Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling](https://mlsanity.com/paper/2104.15046/) (2021)
- [Estimating Social Influence from Observational Data](https://mlsanity.com/paper/2204.01633/) (2022)
- [Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks](https://mlsanity.com/paper/2410.09696/) (2024)
- [Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data](https://mlsanity.com/paper/2410.18425/) (2024)
- [Privacy Preserving Inference of Personalized Content for Out of Matrix Users](https://mlsanity.com/paper/2508.14905/) (2025)
- [Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregation](https://mlsanity.com/paper/2308.08060/) (2023)
- [Efficient algorithms for regularized Poisson Non-negative Matrix Factorization](https://mlsanity.com/paper/2404.16505/) (2024)

## Abstract (arXiv)

We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchases). In contrast to traditional matrix factorization approaches, Poisson factorization implicitly models each user's limited attention to consume items. Moreover, because of the mathematical form of the Poisson likelihood, the model needs only to explicitly consider the observed entries in the matrix, leading to both scalable computation and good predictive performance. We develop a variational inference algorithm for approximate posterior inference that scales up to massive data sets. This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations. We apply our method to large real-world user data containing users rating movies, users listening to songs, and users reading scientific papers. In all these settings, Bayesian Poisson factorization outperforms state-of-the-art matrix factorization methods.