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HGenPush architecture boosts Kuaishou user engagement by 0.181%

Researchers have developed HGenPush, a novel generative recommendation architecture designed for industrial push notification systems. This system addresses the limitations of existing methods by integrating both content and author recommendations within a unified framework. HGenPush utilizes a hybrid user behavior understanding module and a lightweight multi-token prediction method to improve efficiency and user experience. When deployed on Kuaishou, a large-scale short-video platform, HGenPush demonstrated a 0.181% increase in daily active users. AI

IMPACT This architecture could improve user engagement and retention on content platforms by providing more tailored recommendations.

RANK_REASON The item is an academic paper describing a new architecture and its performance on a specific platform. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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HGenPush architecture boosts Kuaishou user engagement by 0.181%

COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Kun Gai ·

    HGenPush: A Heterogeneous Generative Recommendation Architecture for Industrial Push Notification Systems

    With the explosive growth of content platforms, recommendation systems need to better satisfy user demands to enhance user satisfaction and retention. Taking short-video platforms as an example, users not only seek high-quality content but also trusted authors. Although generativ…