On explainable fuzzy recommenders and their performance evaluation

• Autorzy: Rutkowski Tomasz , Łapa Krystian , Nielek Radosław

Identyfikatory

DOI

10.2478/amcs-2019-0044

• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel approach to the design of explainable recommender systems. It is based on the Wang–Mendel algorithm of fuzzy rule generation. A method for the learning and reduction of the fuzzy recommender is proposed along with feature encoding. Three criteria, including the Akaike information criterion, are used for evaluating an optimal balance between recommender accuracy and interpretability. Simulation results verify the effectiveness of the presented recommender system and illustrate its performance on the MovieLens 10M dataset.

Słowa kluczowe

EN

recommender system; explainable recommendation; fuzzy system; Akaike information criterion

PL

system rekomendacyjny; system rozmyty; kryterium informacyjne Akaike

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2019
• Tom: Vol. 29, no. 3
• Strony: 595--610
• Opis fizyczny: Bibliogr. 24 poz., tab., wykr.

Twórcy

autor

Rutkowski Tomasz

• Senfino, 1412 Broadway 21st floor, New York City, NY 10018, USA; Faculty of Information Technology, Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

autor

Łapa Krystian

• Institute of Computational Intelligence, Częstochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Nielek Radosław

• Faculty of Information Technology, Polish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).