Yuan Sun
The Pennsylvania State University , State College, Pennsylvania, United States
Recommender systems (RS) have become increasingly vital for guiding health actions. While traditional systems filter content based on either demographics, personal history of activities, or preferences of other users, newer systems use social media information to personalize recommendations, based either on the users’ own activities and/or those of their friends on social media platforms. However, we do not know if these approaches differ in their persuasiveness. To find out, we conducted a user study of a fitness plan recommender system (N = 341), wherein participants were randomly assigned to one of six personalization approaches, with half of them given a choice to switch to a different approach. Data revealed that social media-based personalization threatens users’ identity and increases privacy concerns. Users prefer personalized health recommendations based on their own preferences. Choice enhances trust by providing users with a greater sense of agency and lowering their privacy concerns. These findings provide design implications for RS, especially in the preventive health domain.
https://doi.org/10.1145/3544548.3581123
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)