The Internet has become a vital resource for health-related information, advice, and support, with more people turning to websites, social media, and online groups for guidance on various health topics ranging from healthy living to disease diagnosis and treatment. The rise of smart devices connected through the Internet of Things (IoT) has further expanded access to health services, enabling Health Recommender Systems (HRS) to collect patient data and generate personalized recommendations. Despite its potential, the overwhelming volume of health information available through the Internet, smart devices, and connected technologies often hinders users from efficiently accessing timely, relevant, and personalized insights, thereby limiting their ability to fully benefit from these resources. Recommender Systems (RS), widely used in e-commerce and social networks to help users navigate choice overload, have also been adapted to healthcare and the Internet of Things (IoT), where they assist patients, medical professionals, and smart devices in delivering personalized guidance, automation, and decision support. Context-aware collaborative filtering presents a promising solution, allowing recommendations to be dynamically adapted to users' specific circumstances. Integrating contextual information in HRS aim to enhance predictive accuracy and improve the overall quality of the recommendations. Context-aware health recommender systems (CAHRS) incorporate contextual information to dynamically tailor recommendations to individual circumstances. This integration enhances predictive accuracy and significantly improves the overall quality and relevance of health-related suggestions. However, the absence of a standardized framework to design, develop, implement, evaluate and deploy HRS and CAHRS limits their effectiveness and scalability. The paper outlines, evaluates and validates a framework for Context-aware collaborative filtering Recommender System for Health (CARSH) using Differential Context Weighting (DCW) and Particle Swarm Optimization (PSO) and a proposed hybrid context matching similarity measure of Adjusted Triangle multiplying weighted Jaccard (ATMWJ). Their performance is compared with context-aware modeling approaches that rely on weighted matching context similarity. The experimental results show that the proposed hybrid context similarity measure significantly outperforms existing approaches with MAE of 0.5325 and RMSE of 0.7371 compared to the baseline of 0.5901 and 0.7802 respectively on LDOS-CoMoDa dataset. On the Drug Reviews dataset, ATMWJ achieved an F-measure of 0.9239, surpassing the 0.65 baseline and confirming its superior recommendation quality. The experimental results demonstrate that this approach significantly improves prediction accuracy over existing methods, highlighting the importance of selecting an appropriate similarity metric for enhancing recommendation relevance.
Abdi et al. (Thu,) studied this question.