2021

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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