Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation
<|reference_start|>Conditional Quantile Estimation for Uncertain Watch Time in Short-Video Recommendation: 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.<|reference_end|>
@article{lin2024conditional,
    title={Conditional Quantile Estimation for Uncertain Watch Time in Short-Video
  Recommendation},
    author={Chengzhi Lin, Shuchang Liu, Chuyuan Wang, Yongqi Liu},
    journal={arXiv preprint arXiv:2407.12223},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12223},
    primaryClass={cs.LG cs.AI}
}