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

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Artificial Intelligence and Soft Computing (ICAISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10842))

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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

  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-z

    Article  Google Scholar 

  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. Anaissi, M., Goyal, M.: SVM-based association rules for knowledge discovery and classification (2015). https://doi.org/10.1109/APWCCSE.2015.7476236

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  13. Alemeye, F., Getahun, F.: Cloud readiness assessment framework and recommendation system, November 2015. https://doi.org/10.1109/AFRCON.2015.7331995

  14. Burke, R.: Hybrid recommender systems: survey and experiments. User Model User-Adap. Interact 12(4), 331–370 (2002)

    Article  Google Scholar 

  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. Kao, C.-Y., Fahn, C.-S.: A multi-stage learning framework for intelligent system. Expert Syst. Appl. 40(9), 3378–3388 (2013)

    Article  Google Scholar 

  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. Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inform. J. 16, 261–273 (2015)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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_12

    Chapter  Google Scholar 

  21. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22(6), 1414–1427 (1992)

    Article  MathSciNet  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Senfino Technologies, Czestochowa, Poland

    Tomasz Rutkowski, Jakub Romanowski, Piotr Woldan & Paweł Staszewski

  2. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Tomasz Rutkowski & Radosław Nielek

Authors
  1. Tomasz Rutkowski
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  2. Jakub Romanowski
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  3. Piotr Woldan
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  4. Paweł Staszewski
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  5. Radosław Nielek
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Corresponding author

Correspondence to Tomasz Rutkowski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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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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  • DOI: https://doi.org/10.1007/978-3-319-91262-2_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91261-5

  • Online ISBN: 978-3-319-91262-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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