Abstract
In the paper, a neuro-fuzzy structure is implemented as a movie recommender. First, a novel method for transforming nominal values of attributes into a numerical form is proposed. This allows representing the nominal values, e.g. movie genres or actors, in a neuro-fuzzy system designed from scratch using the Mendel-Wang algorithm for rules generation. Several experiments illustrate performance of the neuro-fuzzy recommender.
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References
Nie, F., Wang, H., Huang, H., Ding, C.: Joint schatten lp-norm robust matrix completion for missing value recovery. Knowl. Inf. Syst. 42(3), 525–544 (2013). https://doi.org/10.1007/s10115-013-0713-z
Zhao, K., Pan, L.: A machine learning based trust evaluation framework for online social networks, pp. 69–74 (2015). https://doi.org/10.1109/TrustCom.2014.13
Anaissi, M., Goyal, M.: SVM-based association rules for knowledge discovery and classification (2015). https://doi.org/10.1109/APWCCSE.2015.7476236
Lu, J., Hoi, S., Wang, J., Zhao, P.: Second order online collaborative filtering. J. Mach. Learn. Res. 29, 325–340 (2013)
Zhao, Q., Zhang, Y., Friedman, D., Tan, F.: E-commerce recommendation with personalized promotion, pp. 219–225 (2015). https://doi.org/10.1145/2792838.2800178
Bologna, G., Hayashi, Y.: Characterization of symbolic rules embedded in deep DIMLP networks: a challenge to transparency of deep learning. J. Artif. Intell. Soft Comput. Res. 7(4), 265–286 (2017)
Beg, I., Rashid, T.: Modelling uncertainties in multi-criteria decision making using distance measure and topsis for hesitant fuzzy sets. J. Artif. Intell. Soft Comput. Res. 7(2), 103–109 (2017)
Liu, H., Gegov, A., Cocea, M.: Rule based networks: an efficient and interpretable representation of computational models. J. Artif. Intell. Soft Comput. Res. 7(2), 111–123 (2017)
Riid, A., Preden, J.-S.: Design of fuzzy rule-based classifiers through granulation and consolidation. J. Artif. Intell. Soft Comput. Res. 7(2), 137–147 (2017)
Prasad, M., et al.: A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017)
Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017)
Park, D.H., Kim, H.K., Choi, I.Y., Kim, J.K.: A literature review and classification of recommender systems research. Expert Syst. Appl. 39(11), 10059–10072 (2012)
Alemeye, F., Getahun, F.: Cloud readiness assessment framework and recommendation system, November 2015. https://doi.org/10.1109/AFRCON.2015.7331995
Burke, R.: Hybrid recommender systems: survey and experiments. User Model User-Adap. Interact 12(4), 331–370 (2002)
Baldominos, A., Albacete, E., Saez, Y., Isasi, P.: A scalable machine learning online service for big data real-time analysis (2015). https://doi.org/10.1109/CIBD.2014.7011537
Kao, C.-Y., Fahn, C.-S.: A multi-stage learning framework for intelligent system. Expert Syst. Appl. 40(9), 3378–3388 (2013)
Tsuji, K., Yoshikane, F., Sato, S., Itsumura, H.: Book recommendation using machine learning methods based on library loan records and bibliographic information, pp. 76–79 (2014). https://doi.org/10.1109/IIAI-AAI.2014.26
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)
Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst. Appl. 97, 205–227 (2017)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_12
Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)
Pawlak, Z.: Rough set theory for intelligent industrial applications. In: Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, IPMM 1999 (Cat. No.99EX296), vol. 1, pp. 37–44 (1999)
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Rutkowski, T., Romanowski, J., Woldan, P., Staszewski, P., Nielek, R. (2018). Towards Interpretability of the Movie Recommender Based on a Neuro-Fuzzy Approach. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10842. Springer, Cham. https://doi.org/10.1007/978-3-319-91262-2_66
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