Negative Binomial Matrix Factorization for Recommender Systems
Machine Learning
2018-01-08 v1 Information Retrieval
Machine Learning
Abstract
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson matrix factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom for controlling the dispersion, making NBMF more robust to outliers. We show that NBMF allows to skip traditional pre-processing stages, such as binarization, which lead to loss of information. Two estimation approaches are presented: maximum likelihood and variational Bayes inference. We test our model with a recommendation task and show its ability to predict user tastes with better precision than PF.
Cite
@article{arxiv.1801.01708,
title = {Negative Binomial Matrix Factorization for Recommender Systems},
author = {Olivier Gouvert and Thomas Oberlin and Cédric Févotte},
journal= {arXiv preprint arXiv:1801.01708},
year = {2018}
}