In the study, the researchers recruited 341 people to test six filtering approaches for recommendation systems, including: demographic filtering, which makes suggestions based on preferences of other users similar in age, gender and ethnicity; collaborative filtering, which is based on others who share similar preferences in exercises; and content-based filtering, which relies on the user’s own exercise preferences. These approaches were further categorized into two versions depending on whether the data came from within the app or from social media, which requires access to the user’s social media connections. In addition, one half of the participants were given the choice to change their personalization approach to one of the other five approaches, while the other half were not given such an option.

Participants particularly disliked the approaches that required social media access, said Yuan Sun, a doctoral student in mass communications at Penn State, and the study’s first author.

“What we find is that people really don’t like the social media-based recommendations,” said Sun, who will be joining the University of Florida as an assistant professor in Fall 2023. “There may be a few reasons for that. One might be that they perceive it as a threat to their sense of identity. They think it undermines their essence of being a unique person. Also, the social media-based recommendations trigger privacy concerns.”

Read more:

Swayne, M. (2023, April 24). App users wary of health and fitness recommendations based on social media data. Penn State News. https://www.psu.edu/news/institute-computational-and-data-sciences/story/app-users-wary-health-and-fitness-recommendations/

Sun, Y., Drivas, M., Liao, M., & Sundar, S. S. (2023, April). When Recommender Systems Snoop into Social Media, Users Trust them Less for Health Advice [Conference paper]. CHI ’23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581123