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Natural Noise Management in Recommender Systems Using Fuzzy Tools

  • Raciel Yera
  • Jorge Castro
  • Luis MartínezEmail author
Chapter
Part of the Studies in Computational Intelligence book series (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.

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Ciego de ÁvilaCiego de ÁvilaCuba
  2. 2.University of GranadaGranadaSpain
  3. 3.University of JaénJaénSpain

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