On Explainable Fuzzy Recommenders and their Performance Evaluation

Rutkowski, Tomasz; Łapa, Krystian; Nielek, Radosław

doi:10.2478/amcs-2019-0044

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.

References

Alvarez-Estevez, D., Moret-Bonillo, V. (2018). Revisiting the Wang–Mendel algorithm for fuzzy classification, Expert Systems35(4): 35:312268.10.1111/exsy.12268
Search in Google Scholar Back to article
Bologna, G. and Hayashi, Y. (2017). Characterization of symbolic rules embedded in deep DIMLP networks: A challenge to transparency of deep learning, Journal of Artificial Intelligence and Soft Computing Research7(4): 265–286.10.1515/jaiscr-2017-0019
Search in Google Scholar Back to article
Cpalka, K. (2017). Design of Interpretable Fuzzy Systems, Studies in Computational Intelligence 684, Springer Verlag, Cham.10.1007/978-3-319-52881-6
Search in Google Scholar Back to article
Harper, F.M. and Konstan, J.A. (2015). The MovieLens datasets: History and context, ACM Transactions on Interactive Intelligent Systems5(4):19:1–19:19.10.1145/2827872
Search in Google Scholar Back to article
Ishibuchi H. and T. Nakashima (2001). Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems9(4): 506–515.10.1109/91.940964
Search in Google Scholar Back to article
Ishibuchi H. and T. Yamamoto (2005). Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems13(4): 428–435.10.1109/TFUZZ.2004.841738
Search in Google Scholar Back to article
Jin, Y. (2000). Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems8(2): 212–221.10.1109/91.842154
Search in Google Scholar Back to article
Kuncheva, L. (2000). Fuzzy Classifier Design, Studies in Fuzziness and Soft Computing, Vol. 49, Springer Verlag, New York, NY.10.1007/978-3-7908-1850-5
Search in Google Scholar Back to article
Liu, H., Gegov, A. and Cocea, M. (2017). Rule based networks: An efficient and interpretable representation of computational models, Journal of Artificial Intelligence and Soft Computing Research7(2): 111–123.10.1515/jaiscr-2017-0008
Search in Google Scholar Back to article
Lops, P., Gemmis, M. and Semeraro, G. (2011). Content-based recommender systems: State of the art and trends, in F. Ricci et al. (Eds), Recommender Systems Handbook, Springer, New York, NY, pp.73–105.10.1007/978-0-387-85820-3_3
Search in Google Scholar Back to article
Nauck, D. and R. Kruse (1998). How the learning of rule weights affects the interpretability of fuzzy systems, IEEE International Conference on Fuzzy Systems 1998 (FUZZ-IEEE’98), Ancorage, AK, USA, pp.1235–1240.
Search in Google Scholar Back to article
Portugal, I., Paulo S.C. Alencar and Cowan, D.D. (2018). The use of machine learning algorithms in recommender systems: A systematic review, Expert System Applications97: 205–227.10.1016/j.eswa.2017.12.020
Search in Google Scholar Back to article
Prasad, M., Liu, Y.-T., Li, D.-L., Lin, C.-T., Shah, R.R. and Kaiwartya, O.P. (2017). A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system, Journal of Artificial Intelligence and Soft Computing Research7(1): 33–46.10.1515/jaiscr-2017-0003
Search in Google Scholar Back to article
Riid, A. and Preden, J.-S. (2017). Design of fuzzy rule-based classifiers through granulation and consolidation, Journal of Artificial Intelligence and Soft Computing Research7(2): 137–147.10.1515/jaiscr-2017-0010
Search in Google Scholar Back to article
Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning, Studies in Fuzziness and Soft Computing, Springer Verlag, New York, NY.10.1007/978-3-7908-1802-4
Search in Google Scholar Back to article
Rutkowski, L. (2004). Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation, Kluwer Academic Publishers, Boston, MA/Dordrecht/London.
Search in Google Scholar Back to article
Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer, Berlin.10.1007/978-3-540-76288-1
Search in Google Scholar Back to article
Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P. and Nielek, R. (2018). Towards interpretability of the movie recommender based on a neuro-fuzzy approach, 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, Zakopane, Poland, pp. 752–762.10.1007/978-3-319-91262-2_66
Search in Google Scholar Back to article
Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P., Nielek, R. and Rutkowski, L. (2018). A content-based recommendation system using neuro-fuzzy approach, 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Piscataway, NJ, USA, pp. 1–8, DOI:10.1109/FUZZ-IEEE.2018.8491543.10.1109/FUZZ-.2018.8491543
Open DOI Search in Google Scholar Back to article
Simiński, K. (2010). Rule weights in a neuro-fuzzy system with a hierarchical domain partition, International Journal of Applied Mathematics and Computer Science20(2): 337–347, DOI: 10.2478/v10006-010-0025-3.10.2478/v10006-010-0025-3
Open DOI Search in Google Scholar Back to article
Söderström, T. and Stoica, P. (1989). System Identification, Prentice Hall International, Upper Saddle River, NJ.
Search in Google Scholar Back to article
Wang, L.-X. and Mendel J.M. (1992). Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics: Systems22(6): 1414–1427.10.1109/21.199466
Search in Google Scholar Back to article
Wei, J., He, J., Chen, K., Zhou, Y. and Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items, Expert Systems with Applications69: 29–39.10.1016/j.eswa.2016.09.040
Search in Google Scholar Back to article
Zhang, S., Yao L., Sun A. and Y. Tay (2018). Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys52(1), Article No. 5.10.1145/3285029
Search in Google Scholar Back to article

On Explainable Fuzzy Recommenders and their Performance Evaluation

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