Accurate watch time prediction is crucial for enhancing user engagement in streaming short-video platforms, although it is challenged by complex distribution characteristics across multi-granularity levels. Through systematic analysis of real-world industrial data, we uncover two critical challenges in watch time prediction from a distribution aspect: (1) coarse-grained skewness induced by a significant concentration of quick-skips1, (2) fine-grained diversity arising from various user-video interaction patterns. Consequently, we assume that the watch time follows the Exponential-Gaussian Mixture (EGM) distribution, where the exponential and Gaussian components respectively characterize the skewness and diversity. Accordingly, an Exponential-Gaussian Mixture Network (EGMN) is proposed for the parameterization of EGM distribution, which consists of two key modules: a hidden representation encoder and a mixture parameter generator. We conducted extensive offline experiments on public datasets and online A/B tests on the industrial short-video feeding scenario of Xiaohongshu App to validate the superiority of EGMN compared with existing state-of-the-art methods. Remarkably, comprehensive experimental results have proven that EGMN exhibits excellent distribution fitting ability across coarse-to-fine-grained levels. We open source related code on Github: https://github.com/BestActionNow/EGMN.

翻译：准确的观看时长预测对于提升短视频流媒体平台的用户参与度至关重要，然而多粒度层级上复杂的分布特性对此构成了挑战。通过对真实工业数据的系统分析，我们从分布角度揭示了观看时长预测面临的两个关键挑战：(1) 由大量快速跳过行为引发的粗粒度偏斜性，(2) 由多样化用户-视频交互模式产生的细粒度多样性。因此，我们假设观看时长服从指数-高斯混合分布，其中指数分量和高斯分量分别刻画了偏斜性和多样性特征。基于此，我们提出了指数-高斯混合网络用于该分布的参数化建模，该网络包含两个核心模块：隐表示编码器和混合参数生成器。我们在公开数据集上进行了大量离线实验，并在小红书App的工业级短视频推荐场景中实施了在线A/B测试，以验证EGMN相较于现有先进方法的优越性。值得注意的是，综合实验结果表明EGMN在从粗粒度到细粒度的各个层级上均展现出卓越的分布拟合能力。相关代码已在Github开源：https://github.com/BestActionNow/EGMN。