Users' interactions with recommender systems often involve more than simple acceptance or rejection. We highlight two overlooked states: hesitation, when people deliberate without certainty, and tolerance, when this hesitation escalates into unwanted engagement before ending in disinterest. Across two large-scale surveys (N=6,644 and N=3,864), hesitation was nearly universal, and tolerance emerged as a recurring source of wasted time, frustration, and diminished trust. Analyses of e-commerce and short-video platforms confirm that tolerance behaviors, such as clicking without purchase or shallow viewing, correlate with decreased activity. Finally, an online field study at scale shows that even lightweight strategies treating tolerance as distinct from interest can improve retention while reducing wasted effort. By surfacing hesitation and tolerance as consequential states, this work reframes how recommender systems should interpret feedback, moving beyond clicks and dwell time toward designs that respect user value, reduce hidden costs, and sustain engagement.

翻译：用户与推荐系统的交互往往不止简单的接受或拒绝。我们强调两种被忽视的状态：犹豫（当人们不确定地权衡时）和容忍（当这种犹豫升级为不情愿的参与，最终以失去兴趣告终）。通过两项大规模调查（N=6,644 和 N=3,864），我们发现犹豫几乎普遍存在，而容忍则成为浪费时间、引发挫败感和削弱信任的常见根源。对电商和短视频平台的分析证实，容忍行为（如点击不购买或浅层浏览）与用户活跃度下降相关。最后，一项大规模在线实地研究表明，即使将容忍视为与兴趣不同的状态并采取轻量级策略，也能在减少无效投入的同时提升用户留存。通过揭示犹豫和容忍作为具有重要影响的状态，本研究重构了推荐系统应如何解读用户反馈，超越点击和停留时间等指标，转向尊重用户价值、降低隐性成本并维持长期参与的设计范式。