The significance of estimating video watch time has been highlighted by the rising importance of (short) video recommendation, which has become a core product of mainstream social media platforms. Modeling video watch time, however, has been challenged by the complexity of user-video interaction, such as different user behavior modes in watching the recommended videos and varying watching probability over the video progress bar. Despite the importance and challenges, existing literature on modeling video watch time mostly focuses on relatively black-box mechanical enhancement of the classical regression/classification losses, without factoring in user behavior in a principled manner. In this paper, we for the first time take on a user-centric perspective to model video watch time, from which we propose a white-box statistical framework that directly translates various user behavior assumptions in watching (short) videos into statistical watch time models. These behavior assumptions are portrayed by our domain knowledge on users' behavior modes in video watching. We further employ bucketization to cope with user's non-stationary watching probability over the video progress bar, which additionally helps to respect the constraint of video length and facilitate the practical compatibility between the continuous regression event of watch time and other binary classification events. We test our models extensively on two public datasets, a large-scale offline industrial dataset, and an online A/B test on a short video platform with hundreds of millions of daily-active users. On all experiments, our models perform competitively against strong relevant baselines, demonstrating the efficacy of our user-centric perspective and proposed framework.

翻译：随着（短视频）推荐重要性的日益凸显，视频观看时长估计的意义愈发显著，已成为主流社交媒体平台的核心产品。然而，视频观看时长建模一直面临用户-视频交互复杂性的挑战，例如用户观看推荐视频时的不同行为模式，以及视频进度条上观看概率的动态变化。尽管该问题至关重要且充满挑战，现有关于视频观看时长建模的研究大多聚焦于对经典回归/分类损失的相对黑盒式机制增强，未能以系统化方式纳入用户行为因素。本文首次采用以用户为中心的视角来建模视频观看时长，由此提出一个白盒统计框架，能够将用户在观看（短）视频时的多种行为假设直接转化为统计观看时长模型。这些行为假设源自我们对用户观看视频行为模式的领域知识。为进一步处理用户在视频进度条上非平稳的观看概率，我们采用分桶策略，这不仅有助于遵循视频时长的约束，还能促进连续回归事件（观看时长）与其他二分类事件之间的实际兼容性。我们在两个公共数据集、一个大规模离线工业数据集以及一个拥有数亿日活跃用户的短视频平台在线A/B测试中，对我们的模型进行了广泛验证。在所有实验中，我们的模型相较于相关强基线均表现出竞争力，验证了以用户为中心的视角及所提框架的有效性。