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Dynamic Collaborative Filtering With Compound Poisson Factorization

Author(s): Jerfel, Ghassen; Basbug, Mehmet; Engelhardt, Barbara

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Abstract: Model-based collaborative filtering (CF) analyzes user–item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most CF approaches assume that these latent factors are static; however, in most CF data, user preferences and item perceptions drift over time. Here, we propose a new conjugate and numerically stable dynamic matrix factorization (DCPF) based on hierarchical Poisson factorization that models the smoothly drifting latent factors using gamma-Markov chains. We propose a conjugate gamma chain construction that is numerically stable within our compound-Poisson framework. We then derive a scalable stochastic variational inference approach to estimate the parameters of our model. We apply our model to time-stamped ratings data sets from Netflix, Yelp, and We empirically demonstrate that DCPF achieves a higher predictive accuracy than state-of-the-art static and dynamic factorization algorithms.
Publication Date: 2017
Citation: Jerfel, Ghassen, Mehmet Basbug, and Barbara Engelhardt. “Dynamic Collaborative Filtering With Compound Poisson Factorization.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (2017): 738–47.
ISSN: 1938-7228
Pages: 738 - 747
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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