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English(EN) Index-Free Dynamic Edge Retrieval with Energy-Tail-Aware Partial Scans

新型无索引方法提升动态MIPS性能

研究人员开发了ETAR,一种新颖的无索引动态最大内积搜索(MIPS)方法,旨在平衡查询速度和更新的简便性。ETAR通过关注具有最大平方值的查询坐标,将其余部分视为低幅度尾部,并从这些保留的坐标估计相似性来实现这一点。该方法在各种数据集上表现强劲,在静态数据集上实现了99.2%的Recall@10,速度比精确扫描快4倍以上,并且在移动设备上也能实现加速。ETAR在流式工作负载下保持了100%的Recall@10,而无需重建索引。 AI

影响 该方法可以提高大规模向量搜索的效率,这是推荐系统和语义搜索等许多AI应用的核心组成部分。

排序理由 该条目是一篇研究论文,详细介绍了一种新的信息检索方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新型无索引方法提升动态MIPS性能

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该条目是一篇研究论文,详细介绍了一种新的信息检索方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Omar Alhussein ·

    无索引动态边缘检索与能量尾部感知部分扫描

    Dynamic maximum inner-product search (MIPS) returns the $K$ stored vectors with the largest dot products with a query while allowing the dataset to change through insertions, replacements, and deletions. For edge retrieval, the challenge is to achieve high recall and fast queries…