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On Explainable Flexible Fuzzy Recommender and Its Performance Evaluation Using the Akaike Information Criterion

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Neural Information Processing (ICONIP 2019)


In the paper, fuzzy recommender systems are proposed based on the novel method for nominal attribute coding. Several flexibility parameters - subjects to learning - are incorporated to their construction, allowing systems to better represent patterns encoded in data. The learning process does not affect the initial interpretable form of fuzzy recommenders rules. Using the Akaike Information Criterion allows evaluating the trade-off between a number of rules and interpretability which is crucial to provide proper explanations for users.

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– This research was supported by the Polish National Science Center grants 2015/19/B/ST6/03179.

– The project financed under the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence" in the years 2019-2022, project number 020/RID/2018/19, the amount of financing 12,000,000 PLN.

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Rutkowski, T., Łapa, K., Jaworski, M., Nielek, R., Rutkowska, D. (2019). On Explainable Flexible Fuzzy Recommender and Its Performance Evaluation Using the Akaike Information Criterion. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham.

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  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

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