An EANN-Based Recommender System for Drug Recommendation

Hadi Al-Mubasher, Mariette Awad

Abstract: Recommender systems aim to improve the user experience in a world where data and available alternatives are expanding at an unprecedented rate. Integrating Natural Language Processing and Artificial Neural Networks have resulted in better performance when compared to other recommender systems. This paper showcases the optimization of an artificial neural network-based recommender system that is used for drug recommendation, where the optimization process involves adopting ResNet-50 and a Multiple Criteria Decision Making-based recommender system to tune the learning rate of the neural network models on which the system is based. Results show that our proposed approach leads to a system that outperforms the existing similar systems.