研究推荐系统如何影响用户意见，发现主动选择内容可有效防止观点被引导。
The Feedback Loop Between Recommendation Systems and Reactive Users
- 构建用户意见与推荐系统互动的反馈模型，分析不同消费策略的影响。
- 被动消费易导致观点偏移，而主动减少或调整消费能有效抑制不良变化。
- 适合关注信息茧房、算法影响与用户自主性的研究者与平台设计者。
推荐系统支撑着众多在线平台，其与用户形成反馈循环：系统通过个性化和热门内容推广以最大化用户参与度，而推荐内容又塑造用户观点或行为，进而影响未来推荐。已有研究表明此类动态会导致用户观点变迁。本文探讨具有自我意识的反应型用户——即意识到所消费内容可能被影响——能否通过主动决定是否参与推荐内容来阻止此类变化。我们首先建立反应型用户意见动态与推荐系统之间的反馈模型，研究三种策略下的动态表现：固定内容消费（被动策略），以及递减或自适应递减内容消费（反应型策略）。理论分析表明，反应型策略可在保持从平台获取收益的同时，有效防止或限制不利的观点偏移。数值实验验证并展示了这些理论结论。
原文摘要 · Abstract (English)
Recommendation systems underlie a variety of online platforms. These recommendation systems and their users form a feedback loop, wherein the former aims to maximize user engagement through personalization and the promotion of popular content, while the recommendations shape users' opinions or behaviors, potentially influencing future recommendations. These dynamics have been shown to lead to shifts in users' opinions. In this paper, we ask whether reactive users, who are cognizant of the influence of the content they consume, can prevent such changes by actively choosing whether to engage with recommended content. We first model the feedback loop between reactive users' opinion dynamics and a recommendation system. We study these dynamics under three different policies - fixed content consumption (a passive policy), and decreasing or adaptive decreasing content consumption (reactive policies). We analytically show how reactive policies can help users effectively prevent or restrict undesirable opinion shifts, while still deriving utility from consuming content on the platform. We validate and illustrate our theoretical findings through numerical experiments.
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