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Paper details
Number 3 - September 2019
Volume 29 - 2019
On explainable fuzzy recommenders and their performance evaluation
Tomasz Rutkowski, Krystian Łapa, Radosław Nielek
Abstract
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.
Keywords
recommender systems, explainable recommendations, fuzzy systems, Akaike information criterion