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, given a constraint on the impact on user experience. We introduce two easy-to-implement strategies towards this goal and test their 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 $25\times$ 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 recommendations of other songs are largely preserved, and the performance loss for the platform is negligible. Moreover, the newly gained recommendations are evenly distributed among other artists. Taken together, our findings demonstrate how collective action strategies that are designed to preserve user experience can be effective while not necessarily being adversarial, outlining an important distinction between collective action and data poisoning.