Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations
Machine Learning
2022-02-25 v5 Machine Learning
Abstract
This work explores non-negative low-rank matrix factorization based on regularized Poisson models (PF or "Poisson factorization" for short) for recommender systems with implicit-feedback data. The properties of Poisson likelihood allow a shortcut for very fast computations over zero-valued inputs, and oftentimes results in very sparse factors for both users and items. Compared to HPF (a popular Bayesian formulation of the problem with hierarchical priors), the frequentist optimization-based approach presented here tends to produce better top-N recommendations with significantly shorter fitting times, on top of having sparse solutions.
Cite
@article{arxiv.1811.01908,
title = {Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations},
author = {David Cortes},
journal= {arXiv preprint arXiv:1811.01908},
year = {2022}
}