Rating-based Preference Elicitation for Recommendation of Stress Intervention

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.

[1]  Dietmar Jannach,et al.  Recommending Hotels based on Multi-Dimensional Customer Ratings , 2012, ENTER.

[2]  Gediminas Adomavicius,et al.  Multi-Criteria Recommender Systems , 2011, Recommender Systems Handbook.

[3]  A. Bandura.  Self-efficacy: toward a unifying theory of behavioral change. , 1977, Psychology Review.

[4]  Korra Sathya Babu,et al.  User preference learning in multi-criteria recommendations using stacked auto encoders , 2018, RecSys.

[5]  Katrien Verbert,et al.  Recommender Systems for Health Informatics: State-of-the-Art and Future Perspectives , 2016, Machine Learning for Health Informatics.

[6]  Christoph Trattner,et al.  Towards Health (Aware) Recommender Systems , 2017, DH.

[7]  Gediminas Adomavicius,et al.  Incorporating contextual information in recommender systems using a multidimensional approach , 2005, TOIS.

[8]  Jürgen Ziegler,et al.  Multi-Criteria Rating-Based Preference Elicitation in Health Recommender Systems , 2018, HealthRecSys@RecSys.

[9]  Nikos Manouselis,et al.  Analysis and Classification of Multi-Criteria Recommender Systems , 2007, World Wide Web.

[10]  Dietmar Jannach,et al.  Accuracy improvements for multi-criteria recommender systems , 2012, EC '12.

[11]  I. Rosenstock.  Historical Origins of the Health Belief Model , 1974 .

[12]  B. J. Fogg,et al.  A behavior model for persuasive design , 2009, Persuasive '09.

[13]  Manuel Lama,et al.  Applying Multicriteria Algorithms to Restaurant Recommendation , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[14]  E. A. Locke,et al.  Building a practically useful theory of goal setting and task motivation. A 35-year odyssey. , 2002, The American psychologist.