Creator:

Rutkowski, Tomasz ; Łapa, Krystian ; Nielek, Radosław

Contributor:

Kulczycki, Piotr - ed. ; Kacprzyk, Janusz - ed. ; Kóczy, László T. - ed. ; Mesiar, Radko - ed.

Title:

On explainable fuzzy recommenders and their performance evaluation

Subtitle:

.

Group publication title:

AMCS, volume 29 (2019)

Subject and Keywords:

recommender systems ; explainable recommendations ; fuzzy systems ; Akaike information criterion

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2019

Resource Type:

artykuł

DOI:

10.2478/amcs-2019-0044

Pages:

595-610

Source:

AMCS, volume 29, number 3 (2019) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego