# Recurrent Poisson Factorization for Temporal Recommendation

arXiv: https://arxiv.org/abs/1703.01442 · HTML: https://mlsanity.com/paper/1703.01442/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2017.

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

- [Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling](https://mlsanity.com/paper/2102.00089/) (2021)
- [Towards Psychologically-Grounded Dynamic Preference Models](https://mlsanity.com/paper/2208.01534/) (2022)
- [On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes](https://mlsanity.com/paper/2406.00630/) (2024)
- [Evolving Voices Based on Temporal Poisson Factorisation](https://mlsanity.com/paper/2410.18486/) (2024)
- [Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs](https://mlsanity.com/paper/2102.00093/) (2021)
- [Tracking Brand-Associated Polarity-Bearing Topics in User Reviews](https://mlsanity.com/paper/2301.07183/) (2023)
- [Learning under Commission and Omission Event Outliers](https://mlsanity.com/paper/2501.13599/) (2025)
- [Uncovering Social Network Activity Using Joint User and Topic Interaction](https://mlsanity.com/paper/2506.12842/) (2025)

## Abstract (arXiv)

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.