References
- Alvarez-Estevez, D., Moret-Bonillo, V. (2018). Revisiting the Wang–Mendel algorithm for fuzzy classification, Expert Systems35(4): 35:312268.
- 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.
- Cpalka, K. (2017). Design of Interpretable Fuzzy Systems, Studies in Computational Intelligence 684, Springer Verlag, Cham.
- 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.
- Ishibuchi H. and T. Nakashima (2001). Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems9(4): 506–515.
- Ishibuchi H. and T. Yamamoto (2005). Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems13(4): 428–435.
- Jin, Y. (2000). Fuzzy modeling of high-dimensional systems: Complexity reduction and interpretability improvement, IEEE Transactions on Fuzzy Systems8(2): 212–221.
- Kuncheva, L. (2000). Fuzzy Classifier Design, Studies in Fuzziness and Soft Computing, Vol. 49, Springer Verlag, New York, NY.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Rutkowska, D. (2002). Neuro-Fuzzy Architectures and Hybrid Learning, Studies in Fuzziness and Soft Computing, Springer Verlag, New York, NY.
- Rutkowski, L. (2004). Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation, Kluwer Academic Publishers, Boston, MA/Dordrecht/London.
- Rutkowski, L. (2008). Computational Intelligence: Methods and Techniques, Springer, Berlin.
- 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.
- 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
- 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
- Söderström, T. and Stoica, P. (1989). System Identification, Prentice Hall International, Upper Saddle River, NJ.
- 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.
- 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.
- 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.