As repetition of activities can establish habits and exploration of new ones can provide a healthy variety, we investigate how a recommender system for physical activities can optimally balance these two approaches. We conducted an eight-week user study with 62 physically inactive participants who receive personalized repetition and exploration recommendations in a random order. We distinguish between location, workout, and general activities, and collect participants’ subjective perceptions. Our findings indicate that participants initially preferred exploring general activities, but rated repeating recommendations higher after two weeks. By exploring the optimal transition point from exploration to repetition in personalized recommendations, this study contributes to designing more effective recommender systems for health improvement and healthy habit formation.

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.