<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

On Explainable Fuzzy Recommenders and their Performance Evaluation

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"On Explainable Fuzzy Recommenders and their Performance Evaluation"}

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...

On Explainable Fuzzy Recommenders and their Performance Evaluation

Pubblicato online: 28 set 2019

Pagine: 595 - 610

Ricevuto: 05 mar 2019

Accettato: 29 apr 2019

DOI: https://doi.org/10.2478/amcs-2019-0044

Parole chiave
recommender systems, explainable recommendations, fuzzy systems, Akaike information criterion

© 2019 Tomasz Rutkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

On Explainable Fuzzy Recommenders and their Performance Evaluation

Pubblicato online: 28 set 2019

Pagine: 595 - 610

Ricevuto: 05 mar 2019

Accettato: 29 apr 2019

DOI: https://doi.org/10.2478/amcs-2019-0044

Parole chiaverecommender systems, explainable recommendations, fuzzy systems, Akaike information criterion

© 2019 Tomasz Rutkowski et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Parole chiave
recommender systems, explainable recommendations, fuzzy systems, Akaike information criterion