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Towards Interpretability of the Movie Recommender Based on a Neuro-Fuzzy Approach

  • Tomasz RutkowskiEmail author
  • Jakub Romanowski
  • Piotr Woldan
  • Paweł Staszewski
  • Radosław Nielek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

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.

Keywords

Recommendation systems Neuro-fuzzy systems MovieLens 

References

  1. 1.
    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-zCrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Anaissi, M., Goyal, M.: SVM-based association rules for knowledge discovery and classification (2015).  https://doi.org/10.1109/APWCCSE.2015.7476236
  4. 4.
    Lu, J., Hoi, S., Wang, J., Zhao, P.: Second order online collaborative filtering. J. Mach. Learn. Res. 29, 325–340 (2013)Google Scholar
  5. 5.
    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
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    Alemeye, F., Getahun, F.: Cloud readiness assessment framework and recommendation system, November 2015.  https://doi.org/10.1109/AFRCON.2015.7331995
  14. 14.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model User-Adap. Interact 12(4), 331–370 (2002)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Kao, C.-Y., Fahn, C.-S.: A multi-stage learning framework for intelligent system. Expert Syst. Appl. 40(9), 3378–3388 (2013)CrossRefGoogle Scholar
  17. 17.
    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
  18. 18.
    Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)CrossRefGoogle Scholar
  19. 19.
    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)CrossRefGoogle Scholar
  20. 20.
    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_12CrossRefGoogle Scholar
  21. 21.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)MathSciNetCrossRefGoogle Scholar
  22. 22.
    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)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tomasz Rutkowski
    • 1
    • 2
    Email author
  • Jakub Romanowski
    • 1
  • Piotr Woldan
    • 1
  • Paweł Staszewski
    • 1
  • Radosław Nielek
    • 2
  1. 1.Senfino TechnologiesCzestochowaPoland
  2. 2.Polish-Japanese Academy of Information TechnologyWarsawPoland

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