Senselife: Service Recommendation and Frailty Prevention Through Knowledge Models
Résumé
The aging global population presents unique challenges, particularly in managing frailty—a condition defined by declines in physical, cognitive, and social capacities. This paper introduces Senselife, a recommender system tailored for frailty management in elderly individuals. Senselife leverages hypergraph-based knowledge models to intelligently recommend personalized services aimed at mitigating frailty and enhancing life quality. Our methodology integrates diverse data types through Heterogeneous Information Networks (HINs), allowing for nuanced user-service interactions that significantly improve recommendation accuracy and relevance. This paper details the development of these models, emphasizing the transition from conventional data handling to advanced, knowledge-driven approaches that consider both user and service complexities. By incorporating these sophisticated models, Senselife aims to provide a scalable solution for frailty prevention, offering a significant contribution to personalized elderly care.