@inproceedings{ubo_mods_00191812,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations},
  booktitle = {27th International Conference on Intelligent User Interfaces},
  series = {ACM Conferences},
  year = {2022},
  publisher = {Association for Computing Machinery},
  address = {New York,NY,United States},
  pages = {106–119},
  keywords = {Ability; Behavior change; Difficulty; Elo; Glicko-2; Health recommender systems; Personalization; Rasch; TrueSkill},
  isbn = {9781450391443},
  doi = {10.1145/3490099},
  url = {https://doi.org/10.1145/3490099.3511118},
  language = {en}
}


@phdthesis{ubo_mods_00184619,
  author = {Torkamaan, Helma},
  title = {Health Recommender Systems for Mental Health Promotion},
  year = {2022},
  address = {Duisburg, Essen},
  keywords = {Health recommender systems; Mobile health; Mental health promotion; Mood; Stress; Mood Tracking},
  abstract = {Recommender systems are today an essential part of software applications used in everyday life and facilitate the decision-making process for users by personalizing the options from which they can choose. An emerging and rapidly growing application domain for these systems is health care, and the majority of research contributions related to health recommender systems are about preventive health care. However, some crucial areas in this domain have been mostly overlooked. One such area is mental health promotion, which, despite its critical importance, has a relatively negligible share in existing solutions and research. User stress and mood are fundamental concepts in preventive health care, and proper skills for coping with stress and improving mood are crucial for individual mental well-being, which is the central theme of this dissertation. Health recommender systems have high potential benefits in personalizing health-related recommendations and, especially, engaging users in behavior change processes. A health recommender system for health promotion and behavior change is a holistic system that, ideally, uses techniques from ubiquitous computing to provide pervasive health. Building a health recommender system, therefore, is a multidisciplinary effort that engages various areas, which we summarize as tracking, interacting, and personalizing components, and address them in this dissertation regarding our recommendation domain, stress reduction. In particular, we discuss three major contributions to the problem of building health recommender systems for stress reduction and mood improvement: (1) establishing proper ways to track user mood; (2) building one of the first interactive mobile health recommender system research platforms and providing an extensive holistic dataset for flexible investigation of health recommender systems in the future; and (3) developing dynamic mood and health-aware, user-engaging algorithms and carefully comparing the performance and characteristics of the presented techniques. These contributions were the result of various mixed and longitudinal user studies which engaged with more than 2,500 users. This dissertation brings together for the first time various aspects of user decision-making - such as explicit short-term preferences, health needs, and long-term goals - for a holistic health-aware recommender system. By thoroughly discussing various components, this dissertation presents a roadmap for building health recommender systems, and interactive, mood-aware, and mental health-promoting systems in the future.},
  school = {University of Duisburg-Essen},
  doi = {10.17185/duepublico/76050},
  url = {https://doi.org/10.17185/duepublico/76050}
}


@inproceedings{ubo_mods_00189158,
  author = {Wang, Chunpai and Sahebi, Shaghayegh and Torkamaan, Helma},
  editor = {He, Jing and Unland, Rainer and Santos Jr., Eugene and Tao, Xiaohui and Purohit, Hemant and van den Heuvel, Willem-Jan and Yearwood, John and Cao, Jie},
  title = {STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data},
  booktitle = {WI-IAT ’21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  series = {ACM International Conference Proceeding Series},
  year = {2021},
  publisher = {Association for Computing Machinery},
  address = {New York},
  pages = {453–462},
  keywords = {behavior modeling; stress management; tensor decomposition},
  isbn = {978-1-4503-9115-3},
  doi = {10.1145/3486622},
  url = {https://doi.org/10.1145/3486622.3493967},
  language = {en}
}


@inproceedings{ubo_mods_00170346,
  author = {Elahi, Mehdi and Abdollahpouri, Himan and Mansoury, Masoud and Torkamaan, Helma},
  title = {Beyond Algorithmic Fairness in Recommender Systems},
  booktitle = {UMAP ’21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2021},
  publisher = {Association for Computing Machinery},
  pages = {41–46},
  keywords = {evaluation; fairness; recommender systems},
  isbn = {978-1-4503-8367-7},
  doi = {10.1145/3450614.3461685},
  url = {https://doi.org/10.1145/3450614.3461685},
  language = {en}
}


@inproceedings{ubo_mods_00168133,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System},
  booktitle = {UbiComp ’21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
  year = {2021},
  publisher = {Association for Computing Machinery},
  address = {New York},
  pages = {218–225},
  keywords = {Behavior Change; Health recommender systems; Persuasive Design},
  isbn = {978-1-4503-8461-2},
  doi = {10.1145/3460418},
  url = {https://doi.org/10.1145/3460418.3479330},
  language = {en}
}


@inproceedings{ubo_mods_00166333,
  author = {Herrmanny, Katja and Torkamaan, Helma},
  editor = {Masthoff, Judith and Herder, Eelco and Tintarev, Nava and Tkalčič, Marko},
  title = {Towards a User Integration Framework for Personal Health Decision Support and Recommender Systems},
  booktitle = {Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2021},
  publisher = {Association for Computing Machinery},
  address = {New York},
  pages = {65–76},
  keywords = {Decision support system; Design framwork; Health recommender systems; User in the loop; User integration},
  isbn = {978-1-4503-8366-0},
  doi = {10.1145/3450613.3456816},
  url = {https://doi.org/10.1145/3450613.3456816},
  language = {en}
}


@article{ubo_mods_00159948,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Mobile mood tracking: An investigation of concise and adaptive measurement instruments},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year = {2020},
  publisher = {Association for Computing Machinery},
  volume = {4},
  number = {4},
  pages = {155},
  keywords = {User Compliance},
  issn = {2474-9567},
  doi = {10.1145/3432207}
}


@inproceedings{Torkamaan_2020_exploring,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Exploring chatbot user interfaces for mood measurement: A study of validity and user experience},
  booktitle = {Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},
  year = {2020},
  publisher = {Association for Computing Machinery (ACM)},
  address = {New York},
  pages = {135–138},
  keywords = {PANAS},
  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, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools. \textcopyright 2020 Owner/Author.},
  isbn = {9781450380768},
  doi = {10.1145/3410530.3414395},
  url = {https://dl.acm.org/doi/10.1145/3410530.3414395}
}


@inproceedings{ubo_mods_00140449,
  author = {Torkamaan, Helma and Barbu, Catalin-Mihai and Ziegler, Jürgen},
  editor = {Bogers, Toine and Said, Alan},
  title = {How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations},
  booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems},
  year = {2019},
  publisher = {ACM},
  address = {New York, NY},
  pages = {423–427},
  keywords = {Trust},
  isbn = {978-1-4503-6243-6},
  doi = {10.1145/3298689.3346982},
  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, or creepiness. RS should consider users’ feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users’ expectations of—and their trust in—RS.}
}


@inproceedings{ubo_mods_00136781,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Rating-based preference elicitation for recommendation of stress intervention},
  booktitle = {ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2019},
  publisher = {ACM},
  address = {New York},
  pages = {46–50},
  keywords = {Preference elicitation},
  isbn = {978-1-4503-6021-0},
  doi = {10.1145/3320435.3324990},
  url = {http://dl.acm.org/ft_gateway.cfm?id=3324990&type=pdf}
}


@inproceedings{ubo_mods_00117090,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Multi-criteria rating-based preference elicitation in health recommender systems},
  booktitle = {Proceedings of the Third International Workshop on Health Recommender Systems co-located with Twelfth ACM Conference on Recommender Systems (HealthRecSys’18)},
  series = {CEUR Workshop Proceedings},
  year = {2018},
  month = {oct},
  address = {Aachen},
  volume = {2216},
  pages = {18–23},
  keywords = {Recommender systems},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-2216/healthRecSys18_paper_7.pdf},
  venue = {Vancouver, BC, Canada},
  month_numeric = {10}
}


@inproceedings{ubo_mods_00108262,
  author = {Schäfer, Hanna and Hors-Fraile, Santiago and Karumur, Pavan Raghav and Valdez, Calero André and Said, Alan and Torkamaan, Helma and Ulmer, Tom and Trattner, Christoph},
  title = {Towards Health (Aware) Recommender Systems},
  booktitle = {Proceedings of the 2017 International Conference on Digital Health},
  year = {2017},
  publisher = {ACM},
  address = {New York},
  pages = {157–161},
  keywords = {Patient modeling},
  isbn = {978-1-4503-5249-9},
  doi = {10.1145/3079452.3079499}
}


@inproceedings{ubo_mods_00096389,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  booktitle = {2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, Texas, October 23-26},
  year = {2017},
  title = {A Taxonomy of Mood Research and Its Applications in Computer Science},
  publisher = {Institute of Electrical and Electronics Engineers Inc.},
  address = {Piscataway},
  pages = {421–426},
  isbn = {978-1-5386-0563-9},
  doi = {10.1109/ACII.2017.8273634}
}