An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly.

翻译：准确预测观看时长对于提升视频推荐系统中的用户参与度至关重要。为此，一个有效的观看时长预测框架需满足以下四个特性：首先，尽管观看时长为连续值，它同时也是有序变量，其数值间的相对排序反映了用户偏好的差异。因此，观看时长预测应体现这种序数关系。其次，模型应捕捉视频观看行为之间的条件依赖关系。例如，用户需先观看视频的一半，才能完成完整视频的观看。第三，使用点估计对观看时长建模忽略了模型可能给出高不确定性结果的情况，这可能导致推荐系统中出现不良案例。因此，框架需具备预测不确定性感知能力。第四，实际推荐系统常遭受严重偏差放大问题，因此期望得到无偏差放大的估计。为此，我们提出TPM用于观看时长预测。具体而言，TPM引入观看时长的序数等级，并将问题分解为一系列条件依赖的分类任务，这些任务被组织成树状结构。通过遍历该树可生成观看时长的期望值，同时目标函数中显式引入预测方差作为不确定性度量。此外，我们证明后门调整可无缝集成至TPM中，从而缓解偏差放大问题。在公开数据集上进行了大量离线评估，TPM已在拥有超3亿日活跃用户的真实视频应用"快手"中部署。结果表明，TPM优于现有最先进方法，并显著提升了视频消费量。