Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Reliable Friends via Adversarial Training to Improve Social
  Recommendation   Most of the recent studies of social recommendation assume that people share
similar preferences with their friends and the online social relations are
helpful in improving traditional recommender systems. However, this assumption
is often untenable as the online social networks are quite sparse and a
majority of users only have a small number of friends. Besides, explicit
friends may not share similar interests because of the randomness in the
process of building social networks. Therefore, discovering a number of
reliable friends for each user plays an important role in advancing social
recommendation. Unlike other studies which focus on extracting valuable
explicit social links, our work pays attention to identifying reliable friends
in both the observed and unobserved social networks. Concretely, in this paper,
we propose an end-to-end social recommendation framework based on Generative
Adversarial Nets (GAN). The framework is composed of two blocks: a generator
that is used to produce friends that can possibly enhance the social
recommendation model, and a discriminator that is responsible for assessing
these generated friends and ranking the items according to both the current
user and her friends' preferences. With the competition between the generator
and the discriminator, our framework can dynamically and adaptively generate
reliable friends who can perfectly predict the current user' preference at a
specific time. As a result, the sparsity and unreliability problems of explicit
social relations can be mitigated and the social recommendation performance is
significantly improved. Experimental studies on real-world datasets demonstrate
the superiority of our framework and verify the positive effects of the
generated reliable friends.

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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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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: atom-ph