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

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a collective of fans aiming to promote the visibility of an artist by strategically placing one of their songs in the existing playlists they control. The success of the collective is measured by the increase in test-time recommendations of the targeted song. We introduce an easily implementable strategy towards this goal and test its efficacy on a publicly available recommender system model used in production by a major music streaming platform. Our findings reveal that even small collectives (controlling less than 0.01% of the training data) can achieve up to 25× amplification of recommendations by strategically choosing the position at which to insert the song. Further, we find that the strategy only minimally impairs user experience; recommendations of other songs are largely preserved, and newly gained recommendations are taken from diverse songs of varying popularity levels. Taken together, our findings demonstrate how algorithmic collective action can be effective while not necessarily being adversarial, raising new questions around fairness, incentives, and social dynamics in recommender systems.