两篇新研究论文解决了大规模检索系统中的挑战，重点是提高效率和准确性。第一篇论文 MESH 提出了一种用于异构内容检索的统一框架，该框架增强了新鲜项目的扩展行为并提高了系统吞吐量。第二篇论文介绍了一个用于 Walmart 使用的基于嵌入的检索管道，该管道结合了混合硬负例挖掘和遗留感知蒸馏，以稳定和改进检索性能。 AI
影响 这些论文提供了改进检索系统效率和有效性的新方法，这对于电子商务和内容平台中的搜索和推荐引擎至关重要。
排序理由 两篇在 arXiv 上发表的学术论文，详细介绍了大规模检索系统的新方法。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.12392v1 Announce Type: cross Abstract: Optimizing large-scale retrieval hinges on the ability to efficiently surface candidates across diverse content tiers. However, to capture segments such as fresh and long-tail content, modern systems typically resort to a fragment…
Optimizing large-scale retrieval hinges on the ability to efficiently surface candidates across diverse content tiers. However, to capture segments such as fresh and long-tail content, modern systems typically resort to a fragmented "zoo" of specialized retrieval models. This ope…
Embedding-based retrieval (EBR) is foundational to large-scale e-commerce search, yet its effectiveness is often constrained by the quality of training signals and the representational capacity of the encoder. Standard dual-encoders suffer from a training-inference gap: they are …