Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Cross-domain Recommendation through Probabilistic
  Cluster-level Latent Factor Model--Extended Version   Cross-domain recommendation has been proposed to transfer user behavior
pattern by pooling together the rating data from multiple domains to alleviate
the sparsity problem appearing in single rating domains. However, previous
models only assume that multiple domains share a latent common rating pattern
based on the user-item co-clustering. To capture diversities among different
domains, we propose a novel Probabilistic Cluster-level Latent Factor (PCLF)
model to improve the cross-domain recommendation performance. Experiments on
several real world datasets demonstrate that our proposed model outperforms the
state-of-the-art methods for the cross-domain recommendation task.

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The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th