A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The key to this balance lies in quantifying model uncertainty, which approximates the risk of prediction errors and reveals the limits of the model's current knowledge. On large-scale short-video and livestream platforms, model uncertainty can warn of low-quality recommendations that may lead to disengagement of LAUs and at the same time identify opportunities to diversify content recommendation for HAUs. To leverage this dichotomy, we introduce a unified, production-ready framework that calibrates uncertainty to drive differentiated strategies. Specifically, we implement a model-uncertainty-based risk-averse deboosting policy for LAUs to suppress unreliable recommendations, while employing a risk-seeking Upper Confidence Bound (UCB) strategy for HAUs to encourage exploration. Validated on a major livestream platform, our framework demonstrates significant improvements in retention (active hours) and satisfaction (quality watch time ratio) for LAUs as well as remarkable increases in interest diversity and category coverage for HAUs, proving the value of uncertainty-aware recommendation in industrial settings.

翻译：推荐系统的一个基本挑战在于为低活跃用户保障可靠性，同时为高活跃用户提供多样性。实现这一平衡的关键在于量化模型不确定性，它近似于预测错误的风险，并揭示了模型当前知识的局限性。在大规模短视频和直播平台上，模型不确定性能够预警可能导致低活跃用户流失的低质量推荐，同时识别出为高活跃用户丰富内容推荐的机会。为利用这种二分性，我们引入了一个统一、可直接投产的框架，通过校准不确定性来驱动差异化策略。具体而言，我们为低活跃用户实施了基于模型不确定性的风险规避型降提升策略，以抑制不可靠推荐；同时，对高活跃用户采用风险寻求型上置信界策略，以鼓励探索。该框架在主流直播平台上经过验证，显著提升了低活跃用户的留存（活跃时长）与满意度（优质观看时长比例），并大幅增加了高活跃用户的兴趣多样性与分类覆盖率，证明了工业场景中不确定性感知推荐的价值。