Investigating different recommender algorithms in the domain of physical activity recommendations: a longitudinal between-subjects user study
As mobile health use is often discontinued, there is a need to improve its personalization with recommender system algorithms. This research innovatively investigates the effect of a user-based collaborative filtering and a content-based recommender algorithm for physical activity recommendation in...
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| Vydáno v: | User modeling and user-adapted interaction Ročník 35; číslo 1; s. 6 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Dordrecht
Springer Nature B.V
01.03.2025
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| Témata: | |
| ISSN: | 0924-1868, 1573-1391 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | As mobile health use is often discontinued, there is a need to improve its personalization with recommender system algorithms. This research innovatively investigates the effect of a user-based collaborative filtering and a content-based recommender algorithm for physical activity recommendation in a longitudinal between-subjects user study with objective metrics and subjective perception questions. Eighty-eight physically inactive participants used the Android app with personalized activity and tip recommendations to motivate them to move more, of which 30 participated for at least eight weeks, resulting in 1357 selected and submitted activity recommendations. Our linear mixed model analyses investigate the evolution of objective diversity, and users’ subjective perceptions of the recommendations, star rating feedback, momentary motivation, and physical activity behavior change. These analyses show that the total objective diversity of the generated recommendations was significantly larger in the collaborative group, but suggest that both algorithms performed equally well on the subjective metrics. The findings also suggest that physical activity recommenders should offer increasing diversity over time, as users in both groups preferred higher diversity as more consumptions are submitted. This study emphasizes the value of tracking the evolving diversity of recommendations and highlights the increase in both groups in perceived accuracy, fun, star ratings, and momentary motivation as more consumptions were submitted over time. As such, this research helps understanding how recommender algorithms learn users’ physical activity preferences over time and how people perceive activity recommendations, contributing to better mobile health strategies for physically inactive individuals. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0924-1868 1573-1391 |
| DOI: | 10.1007/s11257-025-09427-3 |