English(EN) DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search

新的DCEO框架优化电子商务搜索以实现长期用户价值

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-26 11:02

研究人员开发了一个名为DCEO（直接因果效应优化）的新框架，以改进电子商务搜索中的长期用户价值建模。这种数据驱动的方法旨在创建与最终用户目标（如累计购买或商品交易总额（GMV））更一致的商品级代理分数。DCEO利用actor-critic框架直接优化因果效应，超越了传统手动调整的融合方案。在大型工业电子商务搜索系统中部署后，DCEO在为期41天的在线A/B测试中，相对于传统的代理，GMV提高了0.36%。 AI

影响这项研究通过更好地使搜索结果与长期用户价值保持一致，有望带来更具个性化和更有效的电子商务搜索体验。

排序理由关于电子商务搜索新优化框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.IR (Information Retrieval) 阅读 →

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本文如何被排名

Signal score: 2 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 关于电子商务搜索新优化框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Haihong Tang · 2026-08-26 11:02

DCEO：电商搜索中长期用户价值建模的直接因果效应优化

Industrial e-commerce search systems ultimately aim to optimize the user-level long-term objective, such as n-day cumulative purchases or gross merchandise value (GMV) per user. However, such objectives are defined at the user level, whereas search ranking is based on item-level …

本文如何被排名

报道来源 [1]

DCEO：电商搜索中长期用户价值建模的直接因果效应优化

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