Users of social media platforms based on recommendation systems (e.g. TikTok, X, YouTube) strategically interact with platform content to influence future recommendations. On some such platforms, users have been documented to form large-scale grassroots movements encouraging others to purposefully interact with algorithmically suppressed content in order to counteractively ``boost'' its recommendation. However, despite widespread documentation of this phenomenon, there is little theoretical work analyzing its impact on the platform or users themselves. We study a game between users and a RecSys, where users (potentially strategically) interact with the content available to them, and the RecSys -- limited by preference learning ability -- provides each user her approximately most-preferred item. We compare recommendations and social welfare when users interact with content according to their personal interests and when a collective of users intentionally interacts with an otherwise suppressed item. We provide sufficient conditions to ensure a pareto improvement in recommendations and strict increases in user social welfare under collective interaction, and provide a robust algorithm to find an effective collective strategy. Interestingly, despite the intended algorithmic protest of these movements, we show that for commonly assumed recommender utility functions, effective collective strategies also improve the utility of the RecSys. Our theoretical analysis is complemented by empirical results of effective collective interaction strategies on the GoodReads dataset and an online survey on how real-world users attempt to influence others' recommendations on RecSys platforms. Our findings examine how and when platforms' recommendation algorithms may incentivize users to collectivize and interact with content in algorithmic protest as well as what this collectivization means for the platform.

翻译：基于推荐系统的社交媒体平台（如TikTok、X、YouTube）用户会策略性地与平台内容互动，以影响未来的推荐。在某些此类平台上，已有记录表明用户会形成大规模草根运动，鼓励其他人有目的地与算法压制的内容互动，以反向“助推”其推荐。然而，尽管这种现象被广泛记录，但分析其对平台或用户自身影响的理论研究却很少。我们研究了用户与推荐系统之间的博弈：用户（可能策略性地）与可获取的内容互动，而推荐系统——受限于偏好学习能力——为每位用户提供其近似最偏好的项目。我们比较了用户根据个人兴趣与内容互动时，以及用户集体故意与受压制项目互动时的推荐效果与社会福利。我们给出了确保集体互动下推荐帕累托改进和用户社会福利严格提升的充分条件，并提供了一种鲁棒算法以寻找有效的集体策略。有趣的是，尽管这些运动旨在进行算法抗议，但我们证明，对于通常假设的推荐系统效用函数，有效的集体策略同样能提升推荐系统的效用。我们的理论分析辅以GoodReads数据集上有效集体互动策略的实证结果，以及一项关于现实用户如何尝试影响推荐系统平台上他人推荐的在线调查。我们的研究结果探讨了平台推荐算法如何以及何时可能激励用户集体化并与内容互动以进行算法抗议，以及这种集体化对平台意味着什么。