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Negative Binomial Matrix Factorization
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DOI：10.1109/LSP.2020.2991613.png)
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Abstract
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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 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 describe a majorization-minimization (MM) algorithm for a maximum likelihood estimation of the parameters. We provide results on a recommendation task and demonstrate the ability of NBMF to efficiently exploit raw data.
Keywords:
Collaborative filtering
majorization-minimization
non-negative matrix factorization
over-dispersion
Poisson factorization
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