Helma Torkamaan, Jürgen Ziegler Abstract Recommender Systems use implicit and explicit user feedback to recommend desired products or items online. When the recommendation item is a task or behavior change activity, several variables, such as the difficulty of the task and users’ ability to achieve it, in addition to user preferences and needs, determine the […]
Chunpai Wang, Shaghayegh Sahebi, Helma Torkamaan Abstract Stress level modeling and predictions are essential in recommending activities and interventions to individuals. While successful stress models have been proposed in the literature, there is still a missing connection between user engagement behaviors, interest in activities, and their stress levels. In this paper, we propose a novel
STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data Read More »
Helma Torkamaan, Jürgen Ziegler Abstract Behavior change for health promotion is a complex process that requires a high level of personalization, which health recommender systems, as an emerging area, have been trying to address. Despite the advantages of behavior change theories in explaining individuals’ behavior and standardizing the behavior change program overall, these theoretical models
Katja Herrmanny, Helma Torkamaan Abstract Supporting personal health with Decision Support Systems (DSS)and, specifically, recommender systems (RS) is a promising and growing area of research. Integrating the user in the loop is vital in such health systems due to the complexity of recommendations, gravity of the decisions and the reliance on user autonomy. However, for
Helma Torkamaan, Jürgen Ziegler Abstract Commonly used mood measures are either lengthy or too complicated for repeated use. Mood tracking research is, therefore, associated with challenges such as user dissatisfaction, fatigue, or dropouts from studies. Previous efforts to improve user experience are mostly ambiguous concerning their validity and the extent of improvement they provide (e.g.,
Mobile Mood Tracking: An Investigation of Concise and Adaptive Measurement Instruments Read More »
Helma Torkamaan, Jürgen Ziegler Abstract 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
Rating-Based Preference Elicitation for Recommendation of Stress Intervention Read More »
Helma Torkamaan, Jürgen Ziegler Abstract A multi-criteria rating looks for important dimensions to more extensively capture an individual’s opinion about a recommended item. Health Recommender Systems (HRS) is considered to be an emerging domain of recommender systems. In HRS, criteria for a multi-criteria preference elicitation of a recommendation have not yet been fully investigated to
Multi-Criteria Rating-Based Preference Elicitation in Health Recommender Systems Read More »
Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, Christoph Trattner Abstract People increasingly use the Internet for obtaining information regarding diseases, diagnoses and available treatments. Currently, many online health portals already provide non-personalized health information in the form of articles. However, it can be challenging to find
Towards Health (Aware) Recommender Systems Read More »