Abstract
Recommender systems are widely used in today’s society, but many of them do not meet users’ needs and therefore fail to reach their full potential. Without careful consideration, such systems can interfere with the natural decision-making process, resulting in the disregard for recommendations provided. Therefore, it is vital to take into account multiple factors, including expertise, time and risk associated with decisions, as well as the system’s context to identify suitable affordances. Furthermore, it is important to consider the algorithmic and digital literacy of the users. This analysis could reveal innovative design opportunities, like combining a recommender system with a digital agent. As a result, it may meet interpersonal needs and facilitate a more natural interaction with the system. Implementing this combination in a digital marketplace could be a promising way to empower users towards an independent life.
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Acknowledgements
Research Project 54959.1 IP-SBM: Involvement. Inclusion. Participation. one11 - a self-learning platform enables a new form of life through smart community building, funded by Innosuisse in collaboration with one11, FHNW school of social work and FHNW school of engineering.
Algorithmic affordances in recommender interfaces, Workshop at the INTERACT Conference with special thanks to the organizers and participants: Chris Detweiler, Shakila Shayan, Ester Bartels, Karine Cardona, Esther van der Stappen, Suzanne van Rossen, Katja Pott, Jürgen Ziegler, Koen van Turnhout
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Pott, K., Smits, A., Agotai, D. (2024). Recognizing the Algorithmic Literacy of Users in XAI - An Example-Based Approach. In: Bramwell-Dicks, A., Evans, A., Winckler, M., Petrie, H., Abdelnour-Nocera, J. (eds) Design for Equality and Justice. INTERACT 2023. Lecture Notes in Computer Science, vol 14536. Springer, Cham. https://doi.org/10.1007/978-3-031-61698-3_20
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