F-EvoRecSys: Fuzzy Inference meets Evolutionary Approach for Personalized Well-being Recommendations

Many citizens nowadays cope with busy and dynamic lifestyles. Adopting or maintaining a healthy lifestyle to prevent chronic diseases or mental disorders is a core societal challenge. The current global pandemic has evidenced more than ever before the critical importance of engaging 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 Recommender Systems for personalized health and well-being have lately become a research trend, particularly for food and physical activity recommendation. This paper presents F-EvoRecSys: an extension of an evolutionary algorithm-driven approach for "healthy bundle" well-being recommendations that incorporates a fuzzy inference engine aimed at improving physical activity recommendations based on users’ exercising habits. An experimental study demonstrates how this can lead to more diversified recommendations. The paper also discusses challenges and future directions for personalized well-being recommender systems under different perspectives.