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

arXiv:2404.04269v1 Announce Type: cross
Abstract: 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 …

abstract artist arxiv case collective control cs.ir cs.lg cs.si fans playlists promote recommender systems songs success systems transformer type visibility