Helma Torkamaan, Jürgen Ziegler
In recent years, recommender systems have emerged as a key component for personalization in health applications. Central in the development of recommender systems is rating-based preference elicitation, based both on single-criterion and multi-criteria rating. Though its use has already been studied in various domains of recommender systems, far too little attention has been paid to preference elicitation in health recommender systems~(HRS). The purpose of this paper is to develop a better understanding of this preference elicitation by studying the criteria that users consider when they rate a health promotion recommendation from HRS, and accordingly, to offer a design solution as a functional feedback model for mobile health applications. This paper investigates the user-perceived importance of various criteria, as well as latent factors for eliciting user feedback on the recommendations. It also reports the relationship of explanation and trust to the overall rating. By aggregating a list of all possible criteria, we further discover that not all criteria are equally important to users, and that the effectiveness of a recommendation plays a dominant role.
Helma Torkamaan and Jürgen Ziegler. 2019. Rating-based Preference Elicitation for Recommendation of Stress Intervention. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’19). Association for Computing Machinery, New York, NY, USA, 46–50. https://doi.org/10.1145/3320435.3324990