让社交媒体推荐更符合用户深思后的偏好，而非即时冲动。
Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections
- 通过轻量提醒引导用户在浏览时反思自身偏好
- 10天实验显示用户反馈更一致，推荐内容更贴近真实偏好
- 适合希望减少信息茧房、主动掌控推荐内容的用户
社交媒体推荐系统常以用户即时行为为优化目标，而非其深思后的偏好。现有方法虽引入显式配置，但用户偏好不断变化，陈述与行为常不一致，需持续调整。然而传统方式依赖用户主动操作，体验负担大，实际使用极少。本文提出Compass系统，通过在浏览过程中触发轻量级实时反思提示，帮助用户基于自身行为梳理偏好，并定期模拟行为信号、直接调整推荐内容实现对齐。我们在YouTube Shorts中嵌入Compass，开展为期10天的实地研究（N=15），结果表明该系统提升了用户反思性与目的性消费，支持迭代偏好修正，显著增强推荐内容与真实偏好的一致性，同时保持了喂养浏览的随意性。
原文摘要 · Abstract (English)
Social media recommendation feeds often optimize for users' immediate impulses rather than preferences they would hold after deeper reflection. Some systems address this misalignment by incorporating users' explicit preferences via a configuration page or in-feed controls instead of just behavioral signals. However, users typically have evolving preferences, and their stated preferences and behavior naturally diverge, necessitating continuous reflection and feed realignment. But existing strategies require the user to take initiative and are often effortful; as a result, in practice they are rarely invoked. We present Compass, a system that aligns a user's feed with their reflective preferences by helping users reflect on and articulate their preferences given their behavior. To enable continuous reflection during everyday browsing, Compass surfaces in-situ reflections via lightweight notifications, while feed alignment is achieved by periodically simulating behavioral signals and directly manipulating feed content. We embedded Compass within YouTube Shorts and compared it against a baseline without continuous support through a 10-day field study (N=15). We found that Compass promoted more reflective and purposeful feed consumption, iterative preference adjustment, and stronger feed alignment, without sacrificing the casual nature of feed browsing.
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