A Review of Personalized Recommender System for Mental Health Interventions

Personalized recommender systems for mental health are becoming indispensable instruments for providing individuals with individualized resources and therapeutic interventions. This study aims to explore the application of recommender systems within the mental health domain through a systematic literature review. The research is guided by three primary questions: 1) What is a recommender system, and what techniques are available within these systems? 2) What techniques and approaches are used explicitly in recommender systems for mental health applications? 3) What are the limitations and challenges in applying recommender systems in the mental health domain? The first step in answering these questions is to give a thorough introduction to recommender systems, covering all the different methods, including content-based filtering, collaborative filtering, knowledge-based filtering, and hybrid approaches. Next, examine the specific techniques and approaches employed in the mental health context, highlighting their unique requirements for adaptation, benefits, and limitations. Ultimately, the research highlights the key limitations and challenges, including data privacy concerns, the need for tailored recommendations, and the complexities of user engagement in mental health environments. By synthesizing current knowledge, this review provides valuable insights into the potential and constraints of recommender systems in supporting mental health, offering guidance for future research and development in this critical area.