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The public availability of collections containing user preferences is ofvital importance for performing offline evaluations in the field of recommendersystems. However, the number of rating datasets is limited because of the costsrequired for their creation and the fear of violating the privacy of the usersby sharing them. For this reason, numerous research attempts investigated thecreation of synthetic collections of ratings using generative approaches.Nevertheless, these datasets are usually not reliable enough for conducting anevaluation campaign. In this paper, we propose a method for creating syntheticdatasets with a configurable number of users that mimic the characteristics ofalready existing ones. We empirically validated the proposed approach byexploiting the synthetic datasets for evaluating different recommenders and bycomparing the results with the ones obtained using real datasets.

All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation