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F-EvoRecSys: An Extended Framework for Personalized Well-Being Recommendations Guided by Fuzzy Inference and Evolutionary Computing

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Abstract

People nowadays deal with busy and dynamic lifestyles on a daily basis. Adopting or maintaining a healthy lifestyle to prevent chronic conditions is therefore a core societal challenge. It is thus critical to engage and motivate citizens with healthy and tailored activities that they like, as a key driver for safeguarding good health from a preventive vantage point, aligned with the pursuance of SDG 3: “good health and well-being”. This is why Health Recommender Systems have recently become a research trend, particularly in the domains of food and physical activity recommendation. In this work, we present F-EvoRecSys: an extension of an evolutionary algorithm-driven solution for “healthy bundle” recommendations to help users improve their well-being. F-EvoRecSys presents the novelty of incorporating a fuzzy inference system with the aim of improving physical activity recommendations, predicated on users’ exercising habit information. Through an experimental study and a live study with real participants, we demonstrate the feasibility of F-EvoRecSys to produce more diversified recommendations, while maintaing a balance between adapting to the user health needs and matching her/his individual preferences. We finally provide a discussion about challenges and future directions for personalized well-being recommender systems, under three points of view: AI and data approaches, role of fuzzy systems, and application domain considerations.

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Notes

  1. https://www.tudogostoso.com.br.

  2. https://www.u4fit.com.

  3. https://www.runnersworld.com.

  4. https://movielens.org.

  5. https://www.yahoo.com/entertainment/movies/.

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Acknowledgements

This research was supported in part by Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Cheng Kung University (NCKU); and Ministry of Science and Technology of Taiwan, Grant Nos. 109-2410-H-034-037-MY2.

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Authors and Affiliations

  1. National Cheng Kung University, Tainan, 70101, Taiwan

    Iván Palomares

  2. Computer Science Department, University of Bristol, Bristol, BS8 1TH, UK

    Hugo Alcaraz-Herrera

  3. Department of Banking and Finance, Chinese Culture University, Taipei, 111, Taiwan

    Kao-Yi Shen

Authors
  1. Iván Palomares
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  2. Hugo Alcaraz-Herrera
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  3. Kao-Yi Shen
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Corresponding authors

Correspondence to Iván Palomares or Kao-Yi Shen.

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Palomares, I., Alcaraz-Herrera, H. & Shen, KY. F-EvoRecSys: An Extended Framework for Personalized Well-Being Recommendations Guided by Fuzzy Inference and Evolutionary Computing. Int. J. Fuzzy Syst. 24, 2783–2797 (2022). https://doi.org/10.1007/s40815-022-01286-z

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  • DOI: https://doi.org/10.1007/s40815-022-01286-z

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