Publications related to Beyond Algorithmic Fairness in Recommender Systems
Recommendations as Challenges: Estimating Required Effort and User Ability for Health Behavior Change Recommendations
Torkamaan, H., & Ziegler, J. (2022). 27th International Conference on Intelligent User Interfaces, 106–119.
Towards Health (Aware) Recommender Systems
Schäfer, H., Hors-Fraile, S., Karumur, P. R., Valdez, C. A., Said, A., Torkamaan, H., Ulmer, T., & Trattner, C. (2017). Proceedings of the 2017 International Conference on Digital Health, 157–161.
Health Recommender Systems for Mental Health Promotion
Torkamaan, H. (2022). [PhD thesis, University of Duisburg-Essen].
Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System
Torkamaan, H., & Ziegler, J. (2021). 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, 218–225.
Towards a User Integration Framework for Personal Health Decision Support and Recommender Systems
Herrmanny, K., & Torkamaan, H. (2021). In J. Masthoff, E. Herder, N. Tintarev, & M. Tkalčič (Eds.), Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 65–76). Association for Computing Machinery.
Multi-criteria rating-based preference elicitation in health recommender systems
Torkamaan, H., & Ziegler, J. (2018). Proceedings of the Third International Workshop on Health Recommender Systems Co-Located with Twelfth ACM Conference on Recommender Systems (HealthRecSys’18), 2216, 18–23.
How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations
Torkamaan, H., Barbu, C.-M., & Ziegler, J. (2019). In T. Bogers & A. Said (Eds.), Proceedings of the 13th ACM Conference on Recommender Systems (pp. 423–427). ACM.
Multi-list interfaces for recommender systems: Survey and future directions
Loepp, B. (2023). Frontiers in Big Data, 6.
STRETCH: Stress and Behavior Modeling with Tensor Decomposition of Heterogeneous Data
Wang, C., Sahebi, S., & Torkamaan, H. (2021). In J. He, R. Unland, E. Santos Jr., X. Tao, H. Purohit, W.-J. van den Heuvel, J. Yearwood, & J. Cao (Eds.), WI-IAT ’21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 453–462). Association for Computing Machinery.
An Interactive Hybrid Approach to Generate Explainable and Controllable Recommendations
Naveed, S. (2021). [PhD thesis, University of Duisburg-Essen].
Exploring chatbot user interfaces for mood measurement: A study of validity and user experience
Torkamaan, H., & Ziegler, J. (2020). Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers, 135–138.
New Insights Towards Developing Recommender Systems
Taghavi, M., Bentahar, J., Bakhtiyari, K., & Hanachi, C. (2018). The Computer Journal, 61(3), 319–348.
User Control in Recommender Systems: Overview and Interaction Challenges
Jannach, D., Naveed, S., & Jugovac, M. (2017). In D. Bridge & H. Stuckenschmidt (Eds.), E-Commerce and Web Technologies (pp. 21–33). Springer International Publishing.
Agent-based Computational Investing Recommender System
Taghavi, M., Bakhtiyari, K., & Scavino, E. (2013). In Proceedings of the 7th ACM Conference on Recommender Systems (pp. 455–458). ACM.