Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Preference Networks: Probabilistic Models for Recommendation Systems   Recommender systems are important to help users select relevant and
personalised information over massive amounts of data available. We propose an
unified framework called Preference Network (PN) that jointly models various
types of domain knowledge for the task of recommendation. The PN is a
probabilistic model that systematically combines both content-based filtering
and collaborative filtering into a single conditional Markov random field. Once
estimated, it serves as a probabilistic database that supports various useful
queries such as rating prediction and top-$N$ recommendation. To handle the
challenging problem of learning large networks of users and items, we employ a
simple but effective pseudo-likelihood with regularisation. Experiments on the
movie rating data demonstrate the merits of the PN.

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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio