Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Contextual Information as Virtual Items on Top-N Recommender
  Systems   Traditionally, recommender systems for the Web deal with applications that
have two dimensions, users and items. Based on access logs that relate these
dimensions, a recommendation model can be built and used to identify a set of N
items that will be of interest to a certain user. In this paper we propose a
method to complement the information in the access logs with contextual
information without changing the recommendation algorithm. The method consists
in representing context as virtual items. We empirically test this method with
two top-N recommender systems, an item-based collaborative filtering technique
and association rules, on three data sets. The results show that our method is
able to take advantage of the context (new dimensions) when it is informative.

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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: eess