Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists

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Research Overview

Performativity in machine learning

Measuring performative power of online search

Performative Prediction: Past and Future

Applications of algorithmic collective action

Living artefact of collective action cases

Large language models and surveys

Questioning the survey responses of LLMs

Folktexts

Evaluating LLMs as risk scores

Soziale Grundlagen der Informatik Members Publications

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists

This work examines how small collectives can amplify recommendations for a targeted song in transformer-based recommender systems with minimal platform performance impact, revealing non-adversarial yet effective strategies that raise broader questions about incentives and social dynamics [].

Members

Soziale Grundlagen der Informatik

Joachim Baumann

Intern

Algorithms and Society

Celestine Mendler-Dünner

Hector Endowed Fellow of the ELLIS Institute

Publications

Social Foundations of Computation Algorithms and Society Conference Paper Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists Baumann, J., Mendler-Dünner, C. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) , The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2024 (Published) arXiv URL BibTeX