Collective-intelligence recommender systems: advancing computer tailoring for health behavior change into the 21st century

Background: What is the next frontier for computer-tailored health communication (CTHC) research? In current CTHC systems, study designers who have expertise in behavioral theory and mapping theory into CTHC systems select the variables and develop the rules that specify how the content should be tailored, based on their knowledge of the targeted population, the literature, and health behavior theories. In collective-intelligence recommender systems (hereafter recommender systems) used by Web 2.0 companies (eg, Netflix and Amazon), machine learning algorithms combine user profiles and continuous feedback ratings of content (from themselves and other users) to empirically tailor content. Augmenting current theory-based CTHC with empirical recommender systems could be evaluated as the next frontier for CTHC. Objective: The objective of our study was to uncover barriers and challenges to using recommender systems in health promotion. Methods: We conducted a focused literature review, interviewed subject experts (n=8), and synthesized the results. Results: We describe (1) limitations of current CTHC systems, (2) advantages of incorporating recommender systems to move CTHC forward, and (3) challenges to incorporating recommender systems into CTHC. Based on the evidence presented, we propose a future research agenda for CTHC systems. Conclusions: We promote discussion of ways to move CTHC into the 21st century by incorporation of recommender systems.