Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Negative Binomial Matrix Factorization for Recommender Systems   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.

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The categories of this paper are: physics
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The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin