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E-commerce search framework boosts new item sales on Taobao

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new retrieval framework called GrowthGR for e-commerce search engines to address the issue of prioritizing popular items over new ones. This framework includes a module to predict an item's long-term transaction value and another that uses this prediction to balance short-term conversion with long-term growth potential. When deployed on Taobao, GrowthGR led to a 5.3% increase in new item gross merchandise volume and a 0.3% rise in overall search GMV, demonstrating its positive impact on the platform's ecosystem. AI

IMPACT Improves e-commerce search by balancing new item discovery with popular item conversion, potentially increasing platform revenue.

RANK_REASON The cluster contains an academic paper detailing a new framework and its successful deployment and results on a real-world platform. [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 →

E-commerce search framework boosts new item sales on Taobao

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The cluster contains an academic paper detailing a new framework and its successful deployment and results on a real-world platform. [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) · Fei Xiao ·

    Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search

    New item growth is critical for maintaining a healthy ecosystem in large-scale e-commerce platforms. However, existing systems tend to prioritize presenting users with already popular items, a phenomenon often referred to as the "Matthew effect". In the context of search retrieva…

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