Negative Binomial Matrix Factorization for Recommender Systems

2018/01/05 by Olivier Gouvert, Gouvert, Olivier, Thomas Oberlin +3
Computer Science · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Face and Expression Recognition #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Speech and Audio Processing

paper · pdf · doi:10.48550/arxiv.1801.01708

openalex publication_date 2018/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• PU Learning for Matrix Completion
• Matrix Factorization Techniques for Recommender Systems
• Negative binomial and mixed Poisson regression
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• A Tutorial on MM Algorithms
• Hierarchical Compound Poisson Factorization
• Dynamic Collaborative Filtering with Compound Poisson Factorization

Related

• Ordinal Non-negative Matrix Factorization for Recommendation
• Scalable Recommendation with Poisson Factorization
• Matrix Factorization Techniques for Recommender Systems
• Does Weighting Improve Matrix Factorization for Recommender Systems?
• Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations