Negative Binomial Matrix Factorization for Recommender Systems
This work addresses the issue of handling over-dispersed data in recommender systems, offering a more robust method for predicting user tastes, though it appears incremental as an extension of existing techniques.
The paper tackled the problem of analyzing over-dispersed count data in recommender systems by introducing negative binomial matrix factorization (NBMF), which improved prediction precision compared to Poisson matrix factorization.
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.