@inproceedings{torkamaan_recommendations_2022, title = {Recommendations as {Challenges}: {Estimating} {Required} {Effort} and {User} {Ability} for {Health} {Behavior} {Change} {Recommendations}}, doi = {10.1145/3490099.3511118}, booktitle = {27th {International} {Conference} on {Intelligent} {User} {Interfaces} ({IUI} ’22)}, author = {Torkamaan, Helma and Ziegler, J\"{u}rgen}, year = {2022}, isbn = {9781450391443}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3490099.3511118}, doi = {10.1145/3490099.3511118}, 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 suitability of the recommendations. This paper focuses on how user ability and task difficulty concepts can be integrated into the recommendation process to personalize health activity recommendations. To this end, we compare five approaches, some borrowed from the sports and gaming world, and explore their application, advantages, and drawbacks. Through a study of two weeks, we obtained a suitable dataset to investigate how these algorithms can be used for a health recommender system (HRS) and which one is the most appropriate choice for an online HRS in terms of characteristics and flexibility required for behavior change related tailoring. We compared this choice with a baseline algorithm as part of a fully functional HRS to assess the feasibility and impact of integrating the user ability and required effort concepts on the user engagement with the recommendations in an online longitudinal study of two weeks. The results overall suggest that such integration is effective, and in addition to realizing health behavior change requirements, it improves user engagement with the recommendations. }, pages = {106–119}, numpages = {14}, keywords = {Personalization, Health recommender systems, Rasch, TrueSkill, Difficulty, Elo, Ability, Glicko-2, Behavior change}, location = {Helsinki, Finland}, series = {IUI '22} } @inproceedings{torkamaan_2021_integrating, title = {{STRETCH}: {Stress} and {Behavior} {Modeling} with {Tensor} {Decomposition} of {Heterogeneous} {Data}}, booktitle = {{IEEE}/{WIC}/{ACM} {International} {Conference} on {Web} {Intelligence} and {Intelligent} {Agent} {Technology} ({WI}-{IAT} ’21)}, author = {Wang, Chunpai and Sahebi, Shaghayegh and Torkamaan, Helma}, year = {2021}, isbn = {9781450391153}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3486622.3493967}, doi = {10.1145/3486622.3493967}, 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 multi-view tensor decomposition method for stress and user behavior modeling with heterogeneous data, which could provide personalized stress tracking and plausible user behavior modeling across time. To the best of our knowledge, it is the first method that could model user stress and behavior at the same time with multiple resources of data, such as stress measurement, activity rating, and engagement. Our experiments show that leveraging multiple resources of data could not only improve predictions with sparse data, but also results in discovering the underlying stress-activity patterns. We demonstrate the effectiveness of our proposed model on the dataset collected via a self-contained stress management mobile application. }, pages = {453–462}, numpages = {10}, keywords = {behavior modeling, stress management, tensor decomposition}, location = {Melbourne, VIC, Australia}, series = {WI-IAT '21} } @inproceedings{torkamaan_2021_integrating, author = {Torkamaan, Helma and Ziegler, J\"{u}rgen}, title = {Integrating {Behavior} {Change} and {Persuasive} {Design} {Theories} into an {Example} {Mobile} {Health} {Recommender} {System}}, year = {2021}, isbn = {9781450384612}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3460418.3479330}, 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 are either overlooked or unreported for the most part in health promotion systems, a small share of them being related to mental well-being. For a health recommender system to personalize interventions, the interventions should be properly designed, and the behavior change aspects should be adequately integrated into the recommendation process. This paper demonstrates an implementation guideline derived from a practical approach in integrating behavior change theories and persuasive design principles into an example mobile-based health recommender system for mental health promotion. This implementation maps a set of relevant theories for designing the health recommender system into a set of requirements using a functional framework. By realizing these requirements, one can assure that the behavior change theories are at the very least considered. This effort serves as a guideline for future implementations and highlights elements that could perhaps be used for other health or recommendation domains and, particularly, user integration purposes.}, booktitle = {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}, pages = {218–225}, numpages = {8} } @inproceedings{hermanny_towards_2021, title = {Towards a {User} {Integration} {Framework} for {Personal} {Health} {Decision} {Support} and {Recommender} {Systems}}, doi = {https://doi.org/10.1145/3450613.3456816}, booktitle = {In {Proceedings} of the 29th {ACM} {Conference} on {User} {Modeling}, {Adaptation} and {Personalization} ({UMAP} ’21)}, publisher = {ACM}, author = {Hermanny, Katja and Torkamaan, Helma}, year = {2021}, isbn = {9781450383660}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, 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 such a purpose, to the best of our knowledge there exists no profound or comprehensive framework nor model to guide system designers, to exploit the full potential of integrating users in the system’s reasoning process by design. In this paper, we present a multifaceted user integration framework in personal health-related DSS and RS. This framework, with three main components, has been derived from an iterative mixed-methods development and evaluation procedure, including expert workshops and extensive multidisciplinary literature reviews. Users are accordingly integrated into the whole process from system reasoning until decision making through the following actionable design strategies: (1) Empower: Enabling them to understand the result generation and implications, (2) Encourage: encouraging them to question and reflect system outcomes and to get involved in the generation process and (3) Engage: enabling them to take an active role by facilitating and providing opportunities for user control. The framework offers support to designers of personal health-related DSS and RS in properly integrating users into their systems. }, pages = {65–76}, numpages = {12} } @inproceedings{torkamaan_2020_mobile, author = {Torkamaan, Helma and Ziegler, J\"{u}rgen}, title = {Mobile Mood Tracking: An Investigation of Concise and Adaptive Measurement Instruments}, year = {2020}, issue_date = {December 2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {4}, number = {4}, url = {https://doi.org/10.1145/3432207}, doi = {10.1145/3432207}, 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., compared to established measures, such as PANAS). This paper investigates the shortening of a self-reported mood measure using smartphones with four independent samples, and provides a baseline for comparing the usability and accuracy of future measures. It first examines whether user self-assessment of overall positive and negative activations with a two-item measure can capture mood as well as I-PANAS-SF. It next examines user's learning effect in repeated usage of the measure. Finally, it introduces the design of an adaptive mood measure that reduces the number of questions based on its prediction of user mood fluctuations. This adaptive measure can potentially capture specific mood states, as well as overall mood. The paper then explores user satisfaction and compliance with this measure in a longitudinal study. The results of this paper reveal that the investigated two-item measure is a valid and reliable tool for capturing a user's overall mood and mood fluctuations. The negative activation from this measure is associated with stress. Our results suggest that the association between mood and stress generally depends on the measure of mood and its items. We discovered that a non-complex self-explanatory measure is fairly resilient for repeated use with respect to the required effort and the accuracy of the measure in both daily and weekly evaluations. Adaptively reducing the length of a mood measure does not seem to impact user compliance but may slightly improve usability. We also noticed that positive and negative activations have a slightly different pattern of behavior with reference to the preceding mood states.}, journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.}, month = {dec}, articleno = {155}, numpages = {30}, keywords = {Stress, Affect, User Compliance, Mood Tracking, Smartphone, Emotion, Interactive Design} } @inproceedings{torkamaan_2020_exploring, author = {Torkamaan, Helma and Ziegler, J\"{u}rgen}, title = {Exploring Chatbot User Interfaces for Mood Measurement: A Study of Validity and User Experience}, year = {2020}, isbn = {9781450380768}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3410530.3414395}, doi = {10.1145/3410530.3414395}, 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.}, booktitle = {Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers}, pages = {135–138}, numpages = {4}, keywords = {affect grid, mood tracking, conversational ESM, PANAS, chatbot}, location = {Virtual Event, Mexico}, series = {UbiComp-ISWC '20} } @inproceedings{elahi_beyond_2021, title = {Beyond {Algorithmic} {Fairness} in {Recommender} {Systems}}, booktitle = {Adjunct {Proceedings} of the 29th {ACM} {Conference} on {User} {Modeling}, {Adaptation} and {Personalization}}, author = {Elahi, Mehdi and Abdollahpouri, Himan and Mansoury, Masoud and Torkamaan, Helma}, title = {Beyond Algorithmic Fairness in Recommender Systems}, year = {2021}, isbn = {9781450383677}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3450614.3461685}, 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 these works have mainly concentrated on the recommendation algorithms and hence measured the fairness from the algorithmic viewpoint. While such viewpoint may still play an important role, it does not necessarily project a comprehensive picture of how the users may perceive the overall fairness of a recommender system. This paper extends the prior works and goes beyond the algorithmic fairness in recommender systems by highlighting the non-algorithmic viewpoint on the fairness in these systems. The paper proposes an evaluation methodology that can be used to assess the fairness of a recommender system perceived by its users. We have adopted a well-known model and re-formulated it to suit the particular characteristics of the recommender systems, and accordingly, their corresponding users. Our proposed methodology can be used in order to elicit the feedback of the users, along with three important dimensions, i.e., Engagement, Representation, and Action & Expression. We have formed a set of survey questions that address the aforementioned dimensions, as a set of examples to assess the fairness in a recommender system. }, pages = {41–46}, numpages = {6} } @inproceedings{torkamaan_2019_how, author = {Torkamaan, Helma and Barbu, Catalin-Mihai and Ziegler, J\"{u}rgen}, title = {How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations}, year = {2019}, isbn = {9781450362436}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3298689.3346982}, doi = {10.1145/3298689.3346982}, booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems}, pages = {423–427}, numpages = {5}, keywords = {trust, recommender systems, personalization, creepiness, emotion}, location = {Copenhagen, Denmark}, series = {RecSys ’19} } @inproceedings{torkamaan_2019_rating-based, author = {Torkamaan, Helma and Ziegler, J\"{u}rgen}, title = {Rating-Based Preference Elicitation for Recommendation of Stress Intervention}, year = {2019}, isbn = {9781450360210}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3320435.3324990}, doi = {10.1145/3320435.3324990}, booktitle = {Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization}, pages = {46–50}, numpages = {5}, keywords = {behavioral intervention, multi-criteria rating, mobile health, preference elicitation, health recommender systems, recommender systems, health applications}, location = {Larnaca, Cyprus}, series = {UMAP ’19} } @inproceedings{torkamaan_2018_multi, title = {Multi-Criteria Rating-Based Preference Elicitation in Health Recommender Systems}, author = {Helma Torkamaan and J{\"u}rgen Ziegler}, booktitle={Proceedings of the 3rd International Workshop on Health Recommender Systems (HealthRecSys'18) co-located with the 12th ACM Conference on Recommender Systems (ACM RecSys 2018)}, pages={18--23}, year={2018}, series = {CEUR Workshop Proceedings}, issn = {1613-0073}, urn = {urn:nbn:de:0074-2216-4}, venue = {Vancouver, BC, Canada} } @INPROCEEDINGS{torkamaan_2017_taxonomy, author={H. {Torkamaan} and J. {Ziegler}}, booktitle={2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)}, title={A taxonomy of mood research and its applications in computer science}, year={2017}, volume={}, number={}, pages={421-426}, keywords={affective computing;behavioural sciences computing;emotion recognition;emotion interchangeably;psychological concept;mood research taxonomy;computer scientists;terms mood;identifying users;affective computing domain;computer science;affective-computing systems;Mood;Affective computing;Taxonomy;Motion pictures;Videos;Speech recognition;Computer science}, doi={10.1109/ACII.2017.8273634}, ISSN={2156-8111}, month={Oct},} @inproceedings{schaefer_2017_towards, author = {Sch\"{a}fer, Hanna and Hors-Fraile, Santiago and Karumur, Raghav Pavan and Calero Valdez, Andr\'{e} and Said, Alan and Torkamaan, Helma and Ulmer, Tom and Trattner, Christoph}, title = {Towards Health (Aware) Recommender Systems}, year = {2017}, isbn = {9781450352499}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3079452.3079499}, doi = {10.1145/3079452.3079499}, booktitle = {Proceedings of the 2017 International Conference on Digital Health}, pages = {157–161}, numpages = {5}, keywords = {patient modeling, health informatics, health recommender systems, disease modeling, online health interventions}, location = {London, United Kingdom}, series = {DH ’17} }