发现推荐系统中用户犹豫与容忍行为，提升体验与留存。
Hesitation and Tolerance in Recommender Systems
- 识别用户犹豫和容忍两类隐藏状态，超越简单点击行为
- 大规模调研显示容忍行为导致时间浪费与信任下降
- 轻量策略可改善留存率，减少无效互动
用户与推荐系统互动常包含非简单接受或拒绝的复杂状态。本文提出两个被忽视的状态：犹豫（用户在不确定中权衡）与容忍（犹豫演变为不愿参与却仍停留）。通过对两个大规模调查（N=6,644 和 N=3,864）的数据分析，发现犹豫几乎普遍，而容忍成为时间浪费、沮丧感和信任削弱的常见原因。电商与短视频平台的实证分析表明，容忍行为如无购买点击或浅层观看，与用户活跃度下降相关。一次大规模在线场实验显示，将容忍视为独立于兴趣的状态并采取轻量干预，可在不增加负担的前提下提升留存率并减少无效投入。本研究揭示了犹豫与容忍的深远影响，推动推荐系统从仅依赖点击与停留时间转向更尊重用户价值的设计，降低隐性成本，维持长期参与。
原文摘要 · Abstract (English)
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.
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