Analyzing accuracy versus diversity in a health recommender system for physical activities : a longitudinal user study
- Author
- Ine Coppens (UGent) , Luc Martens (UGent) and Toon De Pessemier (UGent)
- Organization
- Abstract
- As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a content-based recommender or more diverse recommendations from a user-based collaborative filtering recommender will lead to more motivation to move. An eight-week longitudinal between-subject user study is being conducted with an Android app in which participants receive personalized recommendations for physical activities and tips to reduce sedentary behavior. The objective manipulation check confirmed that the group with collaborative filtering received significantly more diverse recommendations. The subjective manipulation check showed that the content-based group assigned more positive feedback for perceived accuracy and star rating to the recommendations they chose and executed. However, perceived diversity and inspiringness was significantly higher in the content-based group, suggesting that users might experience the recommendations differently. Lastly, momentary motivation for the executed activities and tips was significantly higher in the content-based group. As such, the preliminary results of this longitudinal study suggest that more accurate and less diverse recommendations have better effects on motivating users to move more.
- Keywords
- recommender system, mobile health, physical activity, motivation, content-based, collaborative filtering
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HNAXYEG43PTP5KKGKF4PFSK1
- MLA
- Coppens, Ine, et al. “Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities : A Longitudinal User Study.” Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), edited by Jie Zhang et al., Association for Computing Machinery (ACM), 2023, pp. 1146–51, doi:10.1145/3604915.3610650.
- APA
- Coppens, I., Martens, L., & De Pessemier, T. (2023). Analyzing accuracy versus diversity in a health recommender system for physical activities : a longitudinal user study. In J. Zhang, L. Chen, S. Berkovsky, M. Zhang, T. di Noia, J. Basilico, … Y. Song (Eds.), Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023) (pp. 1146–1151). https://doi.org/10.1145/3604915.3610650
- Chicago author-date
- Coppens, Ine, Luc Martens, and Toon De Pessemier. 2023. “Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities : A Longitudinal User Study.” In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), edited by Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso di Noia, Justin Basilico, Luiz Pizzato, and Yang Song, 1146–51. New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/3604915.3610650.
- Chicago author-date (all authors)
- Coppens, Ine, Luc Martens, and Toon De Pessemier. 2023. “Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities : A Longitudinal User Study.” In Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), ed by. Jie Zhang, Li Chen, Shlomo Berkovsky, Min Zhang, Tommaso di Noia, Justin Basilico, Luiz Pizzato, and Yang Song, 1146–1151. New York: Association for Computing Machinery (ACM). doi:10.1145/3604915.3610650.
- Vancouver
- 1.Coppens I, Martens L, De Pessemier T. Analyzing accuracy versus diversity in a health recommender system for physical activities : a longitudinal user study. In: Zhang J, Chen L, Berkovsky S, Zhang M, di Noia T, Basilico J, et al., editors. Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023). New York: Association for Computing Machinery (ACM); 2023. p. 1146–51.
- IEEE
- [1]I. Coppens, L. Martens, and T. De Pessemier, “Analyzing accuracy versus diversity in a health recommender system for physical activities : a longitudinal user study,” in Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), Singapore, Singapore, 2023, pp. 1146–1151.
@inproceedings{01HNAXYEG43PTP5KKGKF4PFSK1,
abstract = {{As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy. As such, this paper investigates whether more accurate recommendations from a content-based recommender or more diverse recommendations from a user-based collaborative filtering recommender will lead to more motivation to move. An eight-week longitudinal between-subject user study is being conducted with an Android app in which participants receive personalized recommendations for physical activities and tips to reduce sedentary behavior. The objective manipulation check confirmed that the group with collaborative filtering received significantly more diverse recommendations. The subjective manipulation check showed that the content-based group assigned more positive feedback for perceived accuracy and star rating to the recommendations they chose and executed. However, perceived diversity and inspiringness was significantly higher in the content-based group, suggesting that users might experience the recommendations differently. Lastly, momentary motivation for the executed activities and tips was significantly higher in the content-based group. As such, the preliminary results of this longitudinal study suggest that more accurate and less diverse recommendations have better effects on motivating users to move more.}},
author = {{Coppens, Ine and Martens, Luc and De Pessemier, Toon}},
booktitle = {{Proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023)}},
editor = {{Zhang, Jie and Chen, Li and Berkovsky, Shlomo and Zhang, Min and di Noia, Tommaso and Basilico, Justin and Pizzato, Luiz and Song, Yang}},
isbn = {{9798400702419}},
keywords = {{recommender system,mobile health,physical activity,motivation,content-based,collaborative filtering}},
language = {{eng}},
location = {{Singapore, Singapore}},
pages = {{1146--1151}},
publisher = {{Association for Computing Machinery (ACM)}},
title = {{Analyzing accuracy versus diversity in a health recommender system for physical activities : a longitudinal user study}},
url = {{http://doi.org/10.1145/3604915.3610650}},
year = {{2023}},
}
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