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：推荐系统中的一个根本挑战是如何平衡低活跃用户（LAU）的可靠性需求与高活跃用户（HAU）的多样性需求。实现这一平衡的关键在于量化模型不确定性，该指标可近似预测误差的风险并揭示模型当前知识的边界。在大规模短视频与直播平台上，模型不确定性既能预警可能引发LAU用户流失的低质量推荐，同时也能识别出为HAU用户推荐多样化内容的机会。为利用这一二元特性，我们提出统一的生产级框架，通过校准不确定性来驱动差异化策略。具体而言，我们为LAU用户实施基于模型不确定性的风险规避去增强策略以抑制不可靠推荐，同时为HAU用户采用风险寻求型置信上界（UCB）策略以鼓励探索。在主流直播平台上的验证表明，该框架不仅显著提升了LAU用户的留存（活跃时长）与满意度（有效观看时长占比），也大幅增加了HAU用户的兴趣多样性和内容品类覆盖度，实证了不确定性感知推荐在工业场景中的价值。