  Accurately predicting watch time is crucial for optimizing recommendations
and user experience in short video platforms. However, existing methods that
estimate a single average watch time often fail to capture the inherent
uncertainty and diversity in user engagement patterns. In this paper, we
propose the Conditional Quantile Estimation (CQE) framework to model the entire
conditional distribution of watch time. Using quantile regression, CQE
characterizes the complex watch-time distribution for each user-video pair,
providing a flexible and comprehensive approach to understanding user behavior.
We further design multiple strategies to combine the quantile estimates,
adapting to different recommendation scenarios and user preferences. Extensive
offline experiments and online A/B tests demonstrate the superiority of CQE in
watch time prediction and user engagement modeling. In particular, the online
deployment of CQE in KuaiShow has led to significant improvements in key
evaluation metrics, including active days, active users, engagement duration,
and video view counts. These results highlight the practical impact of our
proposed approach in enhancing the user experience and overall performance of
the short video recommendation system. The code will be released after
publication.
