title: Collective-Intelligence Recommender Systems: Advancing Computer Tailoring for Health Behavior Change Into the 21st Century.
abstract: 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.
authors.affiliation: Division of Health Informatics and Implementation Science, Department of Quantitative Health Science, University of Massachusetts Medical School, Worcester, MA, United States. rajani.sadasivam@umassmed.edu.
authors.forename: Rajani Shankar
authors.identifier: http://orcid.org/0000-0001-8406-6207
authors.initials: RS
authors.lastname: Sadasivam
authors.forename: Sarah L
authors.identifier: http://orcid.org/0000-0002-4795-8377
authors.initials: SL
authors.lastname: Cutrona
authors.forename: Rebecca L
authors.identifier: http://orcid.org/0000-0001-6752-5715
authors.initials: RL
authors.lastname: Kinney
authors.forename: Benjamin M
authors.identifier: http://orcid.org/0000-0002-2626-3410
authors.initials: BM
authors.lastname: Marlin
authors.forename: Kathleen M
authors.identifier: http://orcid.org/0000-0002-9491-9872
authors.initials: KM
authors.lastname: Mazor
authors.forename: Stephenie C
authors.identifier: http://orcid.org/0000-0003-3321-6070
authors.initials: SC
authors.lastname: Lemon
authors.forename: Thomas K
authors.identifier: http://orcid.org/0000-0002-2909-4018
authors.initials: TK
authors.lastname: Houston
mesh_terms: D000465:Algorithms; D003201:Computers / Q000639:trends; D005246:Feedback; D015438:Health Behavior; D058015:Health Communication / Q000379:methods* / Q000639:trends; D006801:Humans; D020407:Internet; D000069550:Machine Learning
keywords: computer-tailored health communication; machine learning; recommender systems