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Hi! I am a human-technology interaction and communication researcher who focuses on personalization and persuasion. I conduct studies using so-called recommender systems, which you may know from Netflix or Amazon. These are algorithms and interfaces that present content that fits your past preferences. I mainly do this in domains where people can change their behavior, such as health and food, sustainability, and news. I have a versatile background and combine insights from different disciplines, such as marketing communication, computer science, psychology, and behavioral economics.

I have been involved in teaching 'Research Topics in Recommender Systems' (INFO345) between 2020 and 2023. I currently supervise a few MSc students Information Science and co-supervise PhD students at MediaFutures.

• Starke, A. D., Musto, C., Rapp, A., Semeraro, G., & Trattner, C. (2024). “Tell me why”: Using natural language justifications in a recipe recommender system to support healthier food choices. User Modeling and user-adapted Interaction, 34(2), 407–440.
• Angelsen, A., Starke, A. D., & Trattner, C. (2023). Healthiness and environmental impact of dinner recipes vary widely across developed countries. Nature Food, 4(5), 407-415.
• Starke, A., Asotic, E., & Trattner, C. (2021). "serving Each User": Supporting different eating goals through a multi-list recommender interface. In RecSys 2021 - 15th ACM Conference on Recommender Systems (pp. 124-132). (RecSys 2021 - 15th ACM Conference on Recommender Systems). Association for Computing Machinery, Inc.
• Beyond "one-size-fits-all" platforms: Applying Campbell's paradigm to test personalized energy advice in the Netherlands. Starke, A.D., Willemsen, M.C. and Snijders, C.C.P. Energy Research and Social Science, 2020. 
• With a little help from my peers: Depicting social norms in a recommender interface to promote energy conservation. Starke, A.D., Willemsen, M.C. and Snijders, C.C.P. Proceedings of the 25th International Conference of Intelligent User Interfaces (IUI), 2020. 

• News Recommender Systems and Polarization: Can we affect people's viewpoints towards controversial topics by presenting personalized content?
• Food Recommender Systems and Health: How can we design digital systems that affect what food people like, choose and cook?