Tailoring By “Cloning”: A User-Based Collaborative Filtering Approach to Recommend Effective Antismoking PSAs

• In Event: Anti-Smoking Messages and Campaigns

Abstract

Theoretical and empirical approaches to the design of effective messages to increase healthy and reduce risky behavior have shown only incremental progress. Recommendation systems as employed by companies like Amazon and Netflix, utilize user-based collaborative filtering (UBCF) which holds the potential to leapfrog conventional models of message research and achieve individual-specific tailoring at scale. This paper reports the effectiveness of statistical “cloning” and “contra-cloning”, an innovative test of the applicability of UBCF, in the context of anti-smoking PSAs. With a nationally representative sample of current smokers and an archive of professionally produced PSAs, “clones”/“contra-clones”—other participants sharing “similar”/“dissimilar” rating patterns with the target individual—were found to positively/negatively predict the target individual’s evaluation of a PSA unseen, even after controlling for individual characteristics and the confounding of mere aggregation. The UBCF approach, and recommendation systems broadly, have important implications for health communication research in general and message tailoring in specific.

Authors

• Sijia Yang, University of Pennsylvania

• Poong Oh, U of Southern California - Annenberg School for Communication

• Hyun Suk Kim, University of Pennsylvania

• Minji Kim, U of Pennsylvania

• Joseph N. Cappella, U of Pennsylvania