A survey on personalized health recommender systems for diverse healthcare applications

Abstract

Health recommender systems are computer algorithms designed to suggest personalized health information to individuals based on their unique needs and preferences. These systems use data such as demographic information, lifestyle habits, and medical history to make tailored personal recommendations. This paper aims to critically assess the present state of the field and pinpoint the key trends, challenges, and opportunities for future development. Initially, the various types of health recommender systems, their applications and benefits are analysed. Next, the various techniques, features and challenges in the area of health recommender systems are surveyed. In addition, the application domains and evaluation metrics utilized for system assessment for each contribution are recorded. Finally, this survey delivers valuable perspectives and suggests potential avenues for future research around health recommender systems, aimed at improving the health and well-being of individuals. It highlights the current shortcomings and difficulties in the field, serving as a guide for researchers, professionals, and decision makers in creating a more effective health recommender system.