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New HGE Framework Improves E-commerce GTV Estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Hierarchical GTV Estimation (HGE) to tackle the challenge of estimating Gross Transaction Value (GTV) in e-commerce platforms that use a container-based display paradigm. This framework addresses issues like high label variance, label sparsity, and dynamic item heterogeneity by employing a Hybrid Set-Aware Encoder (HSAE) for modeling item composition and feature interactions. HGE also incorporates a Proxy-Label Learning (PLL) strategy to decompose GTV and a Residual Correction and Ensemble Fusion (RCEF) module to combine item-level and container-level inference. The system has been deployed and demonstrated significant improvements in online A/B tests, increasing Revenue Per Search (RPS) by 1.42%. AI

IMPACT This research offers a novel approach to improving transaction value estimation in e-commerce, potentially impacting how online platforms optimize sales and user experience.

RANK_REASON Academic paper detailing a new framework and its experimental results. [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 HGE Framework Improves E-commerce GTV Estimation

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Academic paper detailing a new framework and its experimental results. [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) · Xingxing Wang ·

    Hierarchical GTV Estimation: Bridging the Gap between Container-Level Ranking and Item-Level Conversion

    Emerging e-commerce platforms, such as live-streaming and Online-to-Offline (O2O) services, inherently operate under a "Container-based" display paradigm. In this setting, a critical granularity mismatch exists: the platform performs ranking and estimation at the container level …

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