User interactions in recommender systems are inherently complex, often involving behaviors that go beyond simple acceptance or rejection. One particularly common behavior is hesitation, where users deliberate over recommended items, signaling uncertainty. Our large-scale surveys, with 6,644 and 3,864 responses respectively, confirm that hesitation is not only widespread but also has a profound impact on user experiences. When users spend additional time engaging with content they are ultimately uninterested in, this can lead to negative emotions, a phenomenon we term as tolerance. The surveys reveal that such tolerance behaviors often arise after hesitation and can erode trust, satisfaction, and long-term loyalty to the platform. For instance, a click might reflect a need for more information rather than genuine interest, and prolonged exposure to unsuitable content amplifies frustration. This misalignment between user intent and system interpretation introduces noise into recommendation training, resulting in suggestions that increase uncertainty and disengagement. To address these issues, we identified signals indicative of tolerance behavior and analyzed datasets from both e-commerce and short-video platforms. The analysis shows a strong correlation between increased tolerance behavior and decreased user activity. We integrated these insights into the training process of a recommender system for a major short-video platform. Results from four independent online A/B experiments demonstrated significant improvements in user retention, achieved with minimal additional computational costs. These findings underscore the importance of recognizing hesitation as a ubiquitous user behavior and addressing tolerance to enhance satisfaction, build trust, and sustain long-term engagement in recommender systems.

翻译：推荐系统中的用户交互本质上具有复杂性，其行为往往超越简单的接受或拒绝。其中一种尤为常见的行为是犹豫，即用户对推荐项目进行斟酌，表现出不确定性。我们分别开展的大规模调查（样本量分别为6,644和3,864份）证实，犹豫不仅普遍存在，而且对用户体验产生深远影响。当用户花费额外时间接触其最终并不感兴趣的内容时，可能引发负面情绪，我们将此现象定义为容忍。调查显示，此类容忍行为常发生于犹豫之后，并可能削弱用户对平台的信任度、满意度及长期忠诚度。例如，一次点击可能仅反映用户需要更多信息而非真正感兴趣，而长时间接触不适宜内容会加剧挫败感。用户意图与系统解读之间的这种错位，给推荐训练过程引入了噪声，导致产生的建议反而增加了用户的不确定性和脱离倾向。为解决这些问题，我们识别了表征容忍行为的信号，并分析了来自电商和短视频平台的数据集。分析表明，容忍行为的增加与用户活跃度的下降存在强相关性。我们将这些发现整合到某主流短视频平台推荐系统的训练流程中。四项独立的在线A/B实验结果表明，该方法以极低的额外计算成本显著提升了用户留存率。这些发现强调了将犹豫视为普遍用户行为的重要性，并指出通过处理容忍问题可有效提升满意度、建立信任并维持推荐系统中的长期参与度。