Anzeige
Erschienen in:
2021 | OriginalPaper | Buchkapitel
43. Recommender Systems Evaluator: A Framework for Evaluating the Performance of Recommender Systems
verfasst von : Paulo V. G. dos Santos, Bruno Tardiole Kuehne, Bruno G. Batista, Dionisio M. Leite, Maycon L. M. Peixoto, Edmilson Marmo Moreira, Stephan Reiff-Marganiec
Erschienen in: ITNG 2021 18th International Conference on Information Technology-New Generations
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Recommender 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.