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

• URL: http://arxiv.org/abs/2508.12665v1
• Date: Mon, 18 Aug 2025 06:56:36 GMT
• Title: Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network
• Abstract summary: Exponential-Gaussian Mixture Network (EGMN) is proposed for the parameterization of EGM distribution.<n> EGMN exhibits excellent distribution fitting ability across coarse-to-fine-grained levels.
• Score: 31.428173036982216
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Hierarchical Exponential-Gaussian Mixtures for Watch-Time Distribution Prediction [74.05279072166697]
  Exponential-Gaussian Mixture Network (EGMN) models the full conditional WT distribution.<n>EGMN is vulnerable to variance collapse, component redundancy, and inactive components.<n>We propose a Hierarchical Exponential-Gaussian Mixture (HEGM) model that addresses these failure modes.
  arXiv  Detail & Related papers  (2026-08-24T15:06:18Z)
• Gaussian Mixture Modeling for Event-Aware Visual Allocation in Long Video Understanding [55.04091863981529]
  Large Vision-Language Models (LVLMs) face significant challenges in long video understanding due to the excessive computational cost and information loss associated with uniform sampling.<n>We propose GMM-EVA, which leverages Gaussian Mixture Models to model event-level structure from discrete frame-wise.<n>A differentiated allocation strategy is then applied to preserve one primary high-resolution per event for high-fidelity detail, while utilizing lower-resolution secondary observations to maintain temporal context and optimize token budgets.
  arXiv  Detail & Related papers  (2026-07-14T09:27:25Z)
• TimeGMM: Single-Pass Probabilistic Forecasting via Adaptive Gaussian Mixture Models with Reversible Normalization [23.69545884322676]
  TimeGMM is a novel framework that captures complex future distributions in a single forward pass.<n>It consistently outperforms state-of-the-art methods, achieving maximum improvements of 22.48% in CRPS and 21.23% in NMAE.
  arXiv  Detail & Related papers  (2026-01-18T07:02:13Z)
• Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency [60.74505433956616]
  continuous-time consistency model (sCM) is theoretically principled and empirically powerful for accelerating academic-scale diffusion.<n>We first develop a parallelism-compatible FlashAttention-2 JVP kernel, enabling sCM training on models with over 10 billion parameters and high-dimensional video tasks.<n>We propose the score-regularized continuous-time consistency model (rCM), which incorporates score distillation as a long-skip regularizer.
  arXiv  Detail & Related papers  (2025-10-09T16:45:30Z)
• Multi-View Oriented GPLVM: Expressiveness and Efficiency [8.459922325396155]
  We introduce a new duality between the spectral density and the kernel function.<n>We derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs.<n>Our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations.
  arXiv  Detail & Related papers  (2025-02-12T09:49:25Z)
• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection [5.714553194279462]
  We show that a real-valued system can be modeled as spectral signal convolution on graph, through which the correlation between unknown variables can be captured. Based on such analysis, we propose graph convolution-enhanced expectation propagation (GCEPNet) with better generalization capacity.
  arXiv  Detail & Related papers  (2024-04-23T10:13:39Z)
• Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI [29.13807697733638]
  We build on the remarkable achievements in generative sampling of natural images. We propose an innovative challenge, potentially overly ambitious, which involves generating samples that resemble images. The statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects.
  arXiv  Detail & Related papers  (2024-04-10T22:35:06Z)
• GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts [75.51612253852002]
  GraphMETRO is a Graph Neural Network architecture that models natural diversity and captures complex distributional shifts. GraphMETRO achieves state-of-the-art results on four datasets from the GOOD benchmark.
  arXiv  Detail & Related papers  (2023-12-07T20:56:07Z)
• Generalized Mixture Model for Extreme Events Forecasting in Time Series Data [10.542258423966492]
  Time Series Forecasting (TSF) is a widely researched topic with broad applications in weather forecasting, traffic control, and stock price prediction. Extreme values in time series often significantly impact human and natural systems, but predicting them is challenging due to their rare occurrence. We propose a novel framework to enhance the focus on extreme events. Specifically, we propose a Deep Extreme Mixture Model with Autoencoder (DEMMA) for time series prediction.
  arXiv  Detail & Related papers  (2023-10-11T12:36:42Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks [21.49394455839253]
  This work is inspired by Latent DirichletAllocation (LDA) We propose a novel model called GaussianMixture Graphical Lasso (MGL) MGL learns the proportionsof signals generated by each mixture component and their parameters iteratively via an EM framework.
  arXiv  Detail & Related papers  (2021-01-13T21:15:30Z)
• GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue [95.23775347605923]
  Building on the success of deep learning, Generative Adversarial Networks (GANs) provide a modern approach to learn a probability distribution from observed samples. GANs often suffer from the mode collapse issue where the generator fails to capture all existing modes of the input distribution. We take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
  arXiv  Detail & Related papers  (2020-09-24T19:34:37Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.