# Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists arXiv: https://arxiv.org/abs/2404.04269 · HTML: https://mlsanity.com/paper/2404.04269/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 74 references that are in the map. - First posted on arXiv 19 Mar 2024. ## Builds on (most cited first, 30 of 30) - [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018) - [Performative Prediction](https://mlsanity.com/paper/2002.06673/) (2020) - [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023) - [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022) - [Multisided Fairness for Recommendation](https://mlsanity.com/paper/1707.00093/) (2017) - [Performative Prediction: Past and Future](https://mlsanity.com/paper/2310.16608/) (2023) - [Data Poisoning Attacks to Deep Learning Based Recommender Systems](https://mlsanity.com/paper/2101.02644/) (2021) - [Performative Power](https://mlsanity.com/paper/2203.17232/) (2022) - [Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems](https://mlsanity.com/paper/1812.01504/) (2018) - [Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction](https://mlsanity.com/paper/2109.01165/) (2021) - [A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers](https://mlsanity.com/paper/1806.00955/) (2018) - [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020) - [Revisiting Adversarially Learned Injection Attacks Against Recommender Systems](https://mlsanity.com/paper/2008.04876/) (2020) - [Algorithmic Collective Action in Machine Learning](https://mlsanity.com/paper/2302.04262/) (2023) - [Practical Data Poisoning Attack against Next-Item Recommendation](https://mlsanity.com/paper/2004.03728/) (2020) - [Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies](https://mlsanity.com/paper/2012.09995/) (2020) - [Rank List Sensitivity of Recommender Systems to Interaction Perturbations](https://mlsanity.com/paper/2201.12686/) (2022) - [Competition, Alignment, and Equilibria in Digital Marketplaces](https://mlsanity.com/paper/2208.14423/) (2022) - [A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems](https://mlsanity.com/paper/2305.06055/) (2023) - [Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders](https://mlsanity.com/paper/2207.11237/) (2022) - [An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation](https://mlsanity.com/paper/1810.01520/) (2018) - [Recommending to Strategic Users](https://mlsanity.com/paper/2302.06559/) (2023) - [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023) - [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023) - [What is fair? Exploring the artists' perspective on the fairness of music streaming platforms](https://mlsanity.com/paper/2106.02415/) (2021) - [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022) - [Automatic Music Playlist Generation via Simulation-based Reinforcement Learning](https://mlsanity.com/paper/2310.09123/) (2023) - [User Strategization and Trustworthy Algorithms](https://mlsanity.com/paper/2312.17666/) (2023) - [Online Algorithmic Recourse by Collective Action](https://mlsanity.com/paper/2401.00055/) (2023) - [Allowing for equal opportunities for artists in music recommendation](https://mlsanity.com/paper/1911.05395/) (2019) ## Built on by (most cited first, 7 of 7) - [Algorithmic Collective Action with Two Collectives](https://mlsanity.com/paper/2505.00195/) (2025) - [Minority Collective Action for User-Side Fairness](https://mlsanity.com/paper/2508.15374/) (2025) - [Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2508.19149/) (2025) - [Look-Ahead Reasoning on Learning Platforms](https://mlsanity.com/paper/2511.14745/) (2025) - [With a Little Help From My Friends: Collective Manipulation in Risk-Controlling Recommender Systems](https://mlsanity.com/paper/2603.28476/) (2026) - [Observation-Free Attacks on Online Learning to Rank](https://mlsanity.com/paper/2509.22855/) (2025) - [A Statistical Framework for Algorithmic Collective Action with Multiple Collectives](https://mlsanity.com/paper/2605.06749/) (2026) ## Abstract (arXiv) We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a music streaming platform where a collective of fans aims to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song with the goal to boost recommendations at test time. The strategies exploit statistical properties of the learner by targeting discontinuities in the recommendations, and leveraging the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that through strategic placement even small collectives (controlling less than 0.01% of the training data) can achieve up to 40× more test time recommendations than an average song with the same number of training set occurrences. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.