Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering   Understanding a user's motivations provides valuable information beyond the
ability to recommend items. Quite often this can be accomplished by perusing
both ratings and review texts, since it is the latter where the reasoning for
specific preferences is explicitly expressed.
  Unfortunately matrix factorization approaches to recommendation result in
large, complex models that are difficult to interpret and give recommendations
that are hard to clearly explain to users. In contrast, in this paper, we
attack this problem through succinct additive co-clustering. We devise a novel
Bayesian technique for summing co-clusterings of Poisson distributions. With
this novel technique we propose a new Bayesian model for joint collaborative
filtering of ratings and text reviews through a sum of simple co-clusterings.
The simple structure of our model yields easily interpretable recommendations.
Even with a simple, succinct structure, our model outperforms competitors in
terms of predicting ratings with reviews.

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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
2
The categories of this paper are: math
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The categories of this paper are: atom-ph