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Title: Dynamic Compound-Poisson Factorization
Authors: Jerfel, Ghassen
Advisors: Engelhardt, Barbara
Department: Computer Science
Class Year: 2016
Abstract: Collaborative Filtering (CF) analyzes previous user-item interactions in order to infer the latent factors that represent user preferences and item characteristics. However, most current collaborative filtering algorithms assume that these latent factors are static while user preferences and item perceptions drift over time. In this paper, we propose a novel Bayesian Dynamic Matrix Factorization model based on Compound Poisson Factorization that models the smoothly drifting latent factors as Gamma chains. We provide a novel approach to Gamma chains to guarantee their conjugacy and numerical stability. We then provide a scalable inference algorithm to learn the parameters. We finally apply our model to timestamped ratings datasets such as Netflix, Yelp, LastFm where we achieve higher predictive accuracy than state-of-the-art static factorization models.
Extent: 22 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Computer Science, 1988-2016

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