Characterization of public datasets for Recommender Systems

Abstract

As Recommender Systems are becoming very common and widespread, there is an increasing need to evaluate their characteristics such as accuracy, diversity, scalability etc. One of the most fruitful ways to do this is by using public datasets with explicit user feedback about the items. In this paper we present and describe more than 20 available datasets covering different domains such as movies, books, music etc. Each dataset is described over a number of attributes such as size, domain, format of the data, type of access. Unfortunately we did not find any information about the quality of the data contained, that remains an open issue. We also refer to examples from the literature about using the datasets to evaluate recommendation algorithms or solutions. Overall aim of the paper is to offer a convenient resource for finding and selecting datasets as a support for the empirical evaluation of recommendation algorithms and techniques.