Algorithmic Collective Action in Recommender Systems: Promoting Songs by
  Reordering Playlists
Joachim Baumann Celestine Mendler-Dünner
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 two
easily implementable strategies towards this goal and test their efficacy on a
publicly available recommender system model released 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 25x amplification of
recommendations by strategically choosing the position at which to insert the
song. We then focus on investigating the externalities of the strategy. We find
that the performance loss for the platform is negligible, and the
recommendations of other songs are largely preserved, minimally impairing the
user experience of participants. Moreover, the costs are evenly distributed
among other artists. Taken together, our findings demonstrate how collective
action strategies can be effective while not necessarily being adversarial,
raising new questions around incentives, social dynamics, and equilibria in
recommender systems.