Publications

The Risks of Using ChatGPT to Obtain Common Safety-Related Information and Advice

Oviedo-Trespalacios, Oscar, Amy E. Peden, Thomas Cole-Hunter, Arianna Costantini, Milad Haghani, J. E. Rod., Sage Kelly, Helma Torkamaan, Amina Tariq, James David Albert Newton, Timothy Gallagher, Steffen Steinert, Ashleigh Filtness, and Genserik Reniers Abstract ChatGPT is a highly advanced AI language model that has gained widespread popularity. It is trained to understand and generate human …

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Mood Measurement on Smartphones: Which Measure, Which Design?

Helma Torkamaan Abstract Mood, often studied using smartphones, influences human perception, judgment, thought, and behavior. Mood measurements on smartphones face challenges concerning the selection of a proper mood measure and its transfer, or translation, into a digital application (app) that is user-engaging. Addressing these challenges, researchers sometimes end up developing a new interaction design and …

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Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations

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 …

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STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data

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 …

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Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System

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 …

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Towards a User Integration Framework for Personal HealthDecision Support and Recommender Systems

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 …

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Beyond Algorithmic Fairness in Recommender Systems

Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan Abstract Fairness is one of the crucial aspects of modern Recommender Systems which has recently drawn substantial attention from the community. Many recent works have addressed this aspect by studying the fairness of the recommendation through different forms of evaluation methodologies and metrics. However, the majority of …

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Mobile Mood Tracking: An Investigation of Concise and Adaptive Measurement Instruments

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., …

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Exploring chatbot user interfaces for mood measurement: a study of validity and user experience

Helma Torkamaan, Jürgen Ziegler Abstract With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user’s mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, …

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How can they know that? A study of factors affecting the creepiness of recommendations

Helma Torkamaan, , Catalin-Mihai Barbu, Jürgen Ziegler Abstract Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, …

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