Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems
Authorsdos Santos, Paulo V.G.
Tardiole Kuehne, Bruno
Batista, Bruno G.
Leite, Dionisio M.
Peixoto, Maycon L.M.
Moreira, Edmilson Marmo
AffiliationUniversity of Derby
Federal University of Itajubá, Itajubá, Brazil
Federal University of Mato Grosso do Sul (UFMS), Ponta Porã, Brazil
Federal University of Bahia (UFBA), Salvador, Brazil
University of Campinas, Campinas, Brazil
University of Derby
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AbstractRecommender systems are filters that suggest products of interest to customers, which may positively impact sales. Nowadays, there is a multitude of algorithms for recommender systems, and their performance varies widely. So it is crucial to choose the most suitable option given a situation, but it is not a trivial task. In this context, we propose the Recommender Systems Evaluator (RSE): a framework aimed to accomplish an offline performance evaluation of recommender systems. We argue that the usage of a proper methodology is crucial when evaluating the available options. However, it is frequently overlooked, leading to inconsistent results. To help appraisers draw reliable conclusions, RSE is based on statistical concepts and displays results intuitively. A comparative study of classical recommendation algorithms is presented as an evaluation, highlighting RSE’s critical features.
Citationdos Santos, P.V., Kuehne, B.T., Batista, B.G., Leite, D.M., Peixoto, M.L., Moreira, E.M. and Reiff-Marganiec, S., (2021). 'Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems'. In Shahram, L. (Ed.) 'ITNG 2021 18th International Conference on Information Technology-New Generations'. New York: Springer, pp. 339-345..
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