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Social comparison-based features are widely used in social computing apps.However, most existing apps are not grounded in social comparison theories anddo not consider individual differences in social comparison preferences andreactions. This paper is among the first to automatically personalize socialcomparison targets. In the context of an m-health app for physical activity, weuse artificial intelligence (AI) techniques of multi-armed bandits. Resultsfrom our user study (n=53) indicate that there is some evidence that motivationcan be increased using the AI-based personalization of social comparison. Thedetected effects achieved small-to-moderate effect sizes, illustrating thereal-world implications of the intervention for enhancing motivation andphysical activity. In addition to design implications for social comparisonfeatures in social apps, this paper identified the personalization paradox, theconflict between user modeling and adaptation, as a key design challenge ofpersonalized applications for behavior change. Additionally, we proposeresearch directions to mitigate this Personalization Paradox.

Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity