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New POEM framework enhances real-time recommendation systems with partial-order modeling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed POEM (Partial-Order Enhanced Modeling), a novel framework for real-time sequential modeling in recommendation systems. This approach addresses the limitations of traditional models by incorporating dynamic user interests and contextual conditions. POEM leverages partial-order relations derived from multi-stage ranking scores, such as predicted click-through rates and watch durations, to construct richer sequences. When deployed on Kuaishou's online traffic, POEM demonstrated significant improvements, increasing average per-user watch time by approximately 0.249% on the KS Single Page and 0.213% on the KS Lite Page. AI

IMPACT Enhances real-time recommendation systems by better capturing dynamic user interests and optimizing for multiple ranking targets.

RANK_REASON Academic paper detailing a new modeling framework for recommendation systems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

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

New POEM framework enhances real-time recommendation systems with partial-order modeling

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Academic paper detailing a new modeling framework for recommendation systems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

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

    POEM: Partial-Order Enhanced Real-Time Sequential Modeling for Recommendation

    Real-time recommendation systems suffer from the dynamic drift of user interests and varying contextual conditions. Conventional sequential recommendation models only exploit static historical click sequences, which fail to capture instant preference changes and overlook structur…

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