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Natural Noise Management in Recommender Systems Using Fuzzy Tools

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Computational Intelligence for Semantic Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 837))

Abstract

Recommender Systems (RSs) are tools focused on suggesting items that match the interests and preferences of a target user. They have been used in several domains such as e-commerce, e-learning, and social networks. These systems require the elicitation of user preferences, which are not always precise because there are external factors such as human errors, or the inherent vagueness associated to human beings; which are usually related to user behaviors. In RSs, such imperfect behaviors are identified as natural noise (NN), and can bias negatively the recommendation, which affects the RS performance. The current chapter presents two fuzzy models for NN management in a flexible way, which guarantees robust modeling of the uncertainty associated to the user profiles. These models are conceived for individual and group recommendation scenarios respectively, as a data preprocessing step before the recommendation generation. Two case studies are developed to show that the proposals lead to improvements in the accuracy of individual and group recommender systems.

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Notes

  1. 1.

    http://grouplens.org/datasets/movielens/100k/.

  2. 2.

    http://prea.gatech.edu.

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Acknowledgements

This research work was partially supported by the Research Project TIN2015-66524-P.

Author information

Authors and Affiliations

  1. University of Ciego de Ávila, Ciego de Ávila, Cuba

    Raciel Yera

  2. University of Granada, Granada, Spain

    Jorge Castro

  3. University of Jaén, Jaén, Spain

    Luis Martínez

Authors
  1. Raciel Yera
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  2. Jorge Castro
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  3. Luis Martínez
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Corresponding author

Correspondence to Luis Martínez .

Editor information

Editors and Affiliations

  1. Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy

    Giovanni Acampora

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  3. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Skellefteå, Sweden

    Athanasios V. Vasilakos

  4. Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy

    Autilia Vitiello

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Yera, R., Castro, J., Martínez, L. (2020). Natural Noise Management in Recommender Systems Using Fuzzy Tools. In: Acampora, G., Pedrycz, W., Vasilakos, A., Vitiello, A. (eds) Computational Intelligence for Semantic Knowledge Management. Studies in Computational Intelligence, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-030-23760-8_1

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