期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2019
卷号：29
期号：3
页码：1-16
DOI：10.2478/amcs-2019-0044
出版社：De Gruyter Open
摘要：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.
关键词：recommender systems; explainable recommendations; fuzzy systems; Akaike information criterion;
