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New PPDL framework forecasts user retention for short-video platforms

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PPDL, a new framework designed to forecast user retention ratios for large-scale short-video platforms. This framework addresses challenges such as channel heterogeneity, global decay and saturation trends, and limited look-back windows. PPDL integrates physical priors with deep learning by decomposing trends using the Weibull distribution modeled by a Multilayer Perceptron, and by using an auxiliary embedding module for residual components to maintain channel identity. A Multiscale Trend-penalized loss function further enhances trend sensitivity. Experiments on industrial-scale datasets demonstrate that PPDL outperforms existing online solutions across various applications. AI

IMPACT This framework could improve budget allocation for user acquisition in short-video platforms by providing more accurate retention forecasts.

RANK_REASON The item is an academic paper detailing a new framework for forecasting user retention ratios. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PPDL framework forecasts user retention for short-video platforms

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The item is an academic paper detailing a new framework for forecasting user retention ratios. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zibo Zhao, Zhengxiong Guan, Chaoli Zhang, Linyuan Geng, Xuanbing Zhu, Zhonglong Zheng, Fan Wu ·

    PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning

    arXiv:2609.13789v1 Announce Type: new Abstract: In multi-channel paid user acquisition, early and accurate prediction of user retention at the channel level is crucial for optimizing budget allocation. User retention curves display a pronounced temporal pattern: an initial period…

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