Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study
Pages 1146 - 1151
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.
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Index Terms
- Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study
Recommendations
Improving Accuracy of Recommender System by Item Clustering
Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
Using past-prediction accuracy in recommender systems
This paper presents a new approach for memory-based collaborative filtering algorithms. In general, user-based rating prediction is a process in which each neighbor suggests a rating for the target item and the suggestions are combined by weighting the ...
Incorporating user rating credibility in recommender systems
AbstractThere have been many research efforts aimed at improving recommendation accuracy with Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users’ rating behaviors. In ...
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September 2023
1406 pages
ISBN:9798400702419
DOI:10.1145/3604915
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- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
- SIGecom: Special Interest Group on Economics and Computation
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Association for Computing Machinery
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Published: 14 September 2023
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RecSys '23: Seventeenth ACM Conference on Recommender Systems
September 18 - 22, 2023
Singapore, Singapore
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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