Design and Implementation of Proactive Multi-Type Context-Aware Recommender System for Patients Suffering Diabetes
Abdallatif S. Abu-Issa, S. Hajjaj, Salah Aljamal, D. Barghotti, Abdalkarim Awad, M. Hussein, I. Tumar, Abualsoud A. Hanani · 2023
This paper presents a design and implementation of a multi-type, proactive and context-aware recommender system that supports a healthy lifestyle for patients who suffer Diabetes mellitus. The main features of this recommender system includes the consideration of users’ context while generating recommendations, its ability to recommend multi-types in the same time. The recommendation types include food, drink, physical exercise, and medication. Furthermore, the proposed recommender system is proactive, where the recommendations are pushed to the users, based on the context, without explicit query by them. A prototype was developed for the system, as well as simple Android mobile application. Artificial Neural Network was used to train the system. The results show an overall accuracy of 89.5%.