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On Explainable Fuzzy Recommenders and their Performance Evaluation

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International Journal of Applied Mathematics and Computer Science
Information Technology for Systems Research (special section, pp. 427-515), Piotr Kulczycki, Janusz Kacprzyk, László T. Kóczy, Radko Mesiar (Eds.)

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Alvarez-Estevez, D., Moret-Bonillo, V. (2018). Revisiting the Wang–Mendel algorithm for fuzzy classification, Expert Systems35(4): 35:312268.10.1111/exsy.12268Search in Google Scholar

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-2017-0019Search in Google Scholar

Cpalka, K. (2017). Design of Interpretable Fuzzy Systems, Studies in Computational Intelligence 684, Springer Verlag, Cham.10.1007/978-3-319-52881-6Search in Google Scholar

Harper, F.M. and Konstan, J.A. (2015). The MovieLens datasets: History and context, ACM Transactions on Interactive Intelligent Systems5(4):19:1–19:19.10.1145/2827872Search in Google Scholar

Ishibuchi H. and T. Nakashima (2001). Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems9(4): 506–515.10.1109/91.940964Search in Google Scholar

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.2004.841738Search in Google Scholar

Jin, Y. (2000). Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems8(2): 212–221.10.1109/91.842154Search in Google Scholar

Kuncheva, L. (2000). Fuzzy Classifier Design, Studies in Fuzziness and Soft Computing, Vol. 49, Springer Verlag, New York, NY.10.1007/978-3-7908-1850-5Search in Google Scholar

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-2017-0008Search in Google Scholar

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_3Search in Google Scholar

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

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.2017.12.020Search in Google Scholar

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-2017-0003Search in Google Scholar

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-2017-0010Search in Google Scholar

Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning, Studies in Fuzziness and Soft Computing, Springer Verlag, New York, NY.10.1007/978-3-7908-1802-4Search in Google Scholar

Rutkowski, L. (2004). Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation, Kluwer Academic Publishers, Boston, MA/Dordrecht/London.Search in Google Scholar

Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer, Berlin.10.1007/978-3-540-76288-1Search in Google Scholar

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_66Search in Google Scholar

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-.2018.8491543Open DOISearch in Google Scholar

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/v10006-010-0025-3Open DOISearch in Google Scholar

Söderström, T. and Stoica, P. (1989). System Identification, Prentice Hall International, Upper Saddle River, NJ.Search in Google Scholar

Wang, L.-X. and Mendel J.M. (1992). Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics: Systems22(6): 1414–1427.10.1109/21.199466Search in Google Scholar

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.2016.09.040Search in Google Scholar

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. 5.10.1145/3285029Search in Google Scholar

eISSN:
2083-8492
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Applied Mathematics