A Study of Health Recommender Systems: Analyzing the Advancement of Sustainable Health Recommender Systems Using NLP Techniques

Abstract

Over the years, the amount of healthcare data has immensely increased due to technological development. Although it has provided the user with ease to data access, large amounts of data can be challenging to the user due to information overload. In the case of the healthcare domain, any misinterpreted information may cause a severe situation. Recommender systems are proving to be beneficial due to their use in extracting the required information quickly. In this paper, we conducted an analysis of the existing health recommender systems using NLP techniques. We reviewed the existing solutions for health recommender systems, described and compared them based on their features and algorithms employed, and presented future research directions. Our findings indicate that the popular applications of the health recommender systems developed are in patient-doctor match, recommending drugs, cancer predictions, chronic disease diagnosis, and recommender systems for patients with heart problems. Further, given the knowledge of sustainable products, a recommender system can suggest products with these characteristics.