Abstract
This workshop challenges the machine learning-centric focus of modern recommender systems research by reconnecting the field with its interdisciplinary origins and exploring the non-algorithmic dimensions that are crucial to effective recommendation. It fosters a space for reflective, critical, and creative discussions on recommender systems that embrace human values, user experiences, and societal impact. The workshop emphasizes methodological diversity and invites contributions from psychology, human-computer interaction, ethics, design, and other disciplines.
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CITATION STYLE
Zangerle, E., Said, A., & Bauer, C. (2025). Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems (BEYOND 2025). In RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems (pp. 1360–1361). Association for Computing Machinery, Inc. https://doi.org/10.1145/3705328.3747998
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