Recurrent Poisson Factorization for Temporal Recommendation
Seyed Abbas Hosseini (Sharif University of Technology);Keivan Alizadeh (Sharif University of Technology);Ali Khodadadi (Sharif University of Technology);Ali Arabzadeh (Sharif University of Technology);Mehrdad Farajtabar (Georgia Institute of Technology);Hongyuan Zha (Georgia Institute of Technology);Hamid R. Rabiee (Sharif University of Technology)
Abstract
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 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 offers 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.
