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On explainable fuzzy recommenders and their performance evaluation

Rutkowski Tomasz, Łapa Krystian, Nielek Radosław

International Journal of Applied Mathematics and Computer Science

2019

Vol. 29, no. 3

595--610

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.