Dynamic Poisson Factorization
This work addresses the evolving interests of users in recommender systems, such as for scientific papers, but is incremental as it builds on existing Poisson factorization models.
The authors tackled the problem of static latent factors in recommender systems by proposing dPF, a dynamic matrix factorization model that incorporates time-evolving latent factors using a Kalman filter and Poisson distributions, and demonstrated performance improvements over static and dynamic models on 10 years of arXiv user click data.
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.