Hierarchical Exponential-Gaussian Mixtures for Watch-Time Distribution Prediction

Abstract:Accurate watch-time (WT) prediction is an important requirement for short-video recommendations. Yet WT distributions are near-zero-inflated, long-tailed and multimodal. The recent Exponential-Gaussian Mixture Network (EGMN) models the full conditional WT distribution rather than a single point estimate and achieves state-of-the-art performance. Our large-scale reproduction study reveals that EGMN is vulnerable to variance collapse, component redundancy, and inactive components. We propose a Hierarchical Exponential-Gaussian Mixture (HEGM) model that addresses these failure modes through a hierarchical skip-watch decomposition, KL-based variance regularization, structured initialization, removing the forced Gaussian shift and the entropy regularizer. Across public and large-scale industrial datasets, HEGM improves ranking accuracy and threshold-event prediction, while maintaining competitive point-estimation accuracy and substantially improving mixture stability and interpretability. A 1.5-month production A/B test confirms statistically significant engagement lifts. Our code and models are publicly released at this https URL.

Information Retrieval (cs.IR); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2608.23356 [cs.IR]

(or arXiv:2608.23356v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2608.23356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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