Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: All You Need is Ratings: A Clustering Approach to Synthetic Rating
  Datasets Generation   The public availability of collections containing user preferences is of
vital importance for performing offline evaluations in the field of recommender
systems. However, the number of rating datasets is limited because of the costs
required for their creation and the fear of violating the privacy of the users
by sharing them. For this reason, numerous research attempts investigated the
creation of synthetic collections of ratings using generative approaches.
Nevertheless, these datasets are usually not reliable enough for conducting an
evaluation campaign. In this paper, we propose a method for creating synthetic
datasets with a configurable number of users that mimic the characteristics of
already existing ones. We empirically validated the proposed approach by
exploiting the synthetic datasets for evaluating different recommenders and by
comparing the results with the ones obtained using real datasets.

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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: math