标题： 推荐系统中的用户利他主义 显示英文标题

标题： User Altruism in Recommendation Systems

摘要： 基于推荐系统（RecSys）的社交媒体平台用户（例如 TikTok、X、YouTube）战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，有记录显示用户会形成大规模草根运动，鼓励他人故意与算法压制的内容互动，以“提升”其推荐；我们将这种行为称为用户利他主义。 为了捕捉这种行为，我们研究了用户与 RecSys 之间的博弈，其中用户向 RecSys 提供对其可用内容的（可能被操纵的）偏好，而 RecSys 在数据和计算限制下构建一个低秩近似偏好矩阵，并最终为每位用户提供她（近似）最偏好的项目。 我们比较了用户在真实偏好报告和一类捕捉用户利他主义策略下的社会福利。在我们的理论分析中，我们提供了确保用户利他主义下社会福利严格增加的充分条件，并提供了一种找到有效利他策略的算法。 有趣的是，我们表明，对于通常假设的推荐器效用函数，有效的利他策略也会提高 RecSys 的效用！ 我们展示了我们的结果对多种模型误设具有鲁棒性，从而加强了我们的结论。 我们的理论分析得到了 GoodReads 数据集上有效利他策略的经验结果以及关于现实世界用户如何在 RecSys 中表现出利他主义的在线调查的补充。 总体而言，我们的发现证明了传统 RecSys 可能激励用户形成集体和/或遵循利他策略的原因。 显示英文摘要

摘要： Users of social media platforms based on recommendation systems (RecSys) (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 "boost" its recommendation; we term this behavior user altruism. To capture this behavior, we study a game between users and a RecSys, where users provide the RecSys (potentially manipulated) preferences over the contents available to them, and the RecSys -- limited by data and computation constraints -- creates a low-rank approximation preference matrix, and ultimately provides each user her (approximately) most-preferred item. We compare the users' social welfare under truthful preference reporting and under a class of strategies capturing user altruism. In our theoretical analysis, we provide sufficient conditions to ensure strict increases in user social welfare under user altruism, and provide an algorithm to find an effective altruistic strategy. Interestingly, we show that for commonly assumed recommender utility functions, effectively altruistic strategies also improve the utility of the RecSys! We show that our results are robust to several model misspecifications, thus strengthening our conclusions. Our theoretical analysis is complemented by empirical results of effective altruistic strategies on the GoodReads dataset, and an online survey on how real-world users behave altruistically in RecSys. Overall, our findings serve as a proof-of-concept of the reasons why traditional RecSys may incentivize users to form collectives and/or follow altruistic strategies when interacting with them.