Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network

TL;DR

This paper introduces the Exponential-Gaussian Mixture Network (EGMN), a novel model designed to accurately predict video watch time by capturing complex distribution characteristics across multiple granularities, validated through extensive experiments.

Contribution

The paper proposes the EGMN model that effectively captures skewness and diversity in watch time distribution, addressing challenges in multi-granularity prediction.

Findings

EGMN outperforms existing methods in distribution fitting accuracy.

EGMN demonstrates strong performance in both offline and online evaluations.

The model effectively captures distribution characteristics at multiple granularities.

Abstract

Accurate watch time prediction is crucial for enhancing user engagement in streaming short-video platforms, although it is challenged by complex distribution characteristics across multi-granularity levels. Through systematic analysis of real-world industrial data, we uncover two critical challenges in watch time prediction from a distribution aspect: (1) coarse-grained skewness induced by a significant concentration of quick-skips1, (2) fine-grained diversity arising from various user-video interaction patterns. Consequently, we assume that the watch time follows the Exponential-Gaussian Mixture (EGM) distribution, where the exponential and Gaussian components respectively characterize the skewness and diversity. Accordingly, an Exponential-Gaussian Mixture Network (EGMN) is proposed for the parameterization of EGM distribution, which consists of two key modules: a hidden representation encoder and a mixture parameter generator. We conducted extensive offline experiments on public datasets and online A/B tests on the industrial short-video feeding scenario of Xiaohongshu App to validate the superiority of EGMN compared with existing state-of-the-art methods. Remarkably, comprehensive experimental results have proven that EGMN exhibits excellent distribution fitting ability across coarse-to-fine-grained levels. We open source related code on Github: https://github.com/BestActionNow/EGMN.