研究人员开发了一种新的电子商务搜索系统，通过生成相关的用户意图来提高商品的可发现性。该两阶段架构对常见查询使用大型语言模型，对不常查询使用带有 LoRA 适配器的微调小型语言模型。该系统提高了检索效率，将发现覆盖率从 60% 提高到 80%，同时将推理成本降低了约 30%。这种方法旨在通过展示长尾和新兴产品来平衡市场。 AI
影响 通过改善产品发现和潜在地平衡长尾商品的市场曝光来增强电子商务搜索。
排序理由 该集群包含一篇详细介绍新的电子商务搜索系统的研究论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2607.27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction…
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the dis…
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the dis…