JAG：联合属性图用于过滤最近邻搜索
JAG: Joint Attribute Graphs for Filtered Nearest Neighbor Search
AI总结：
JAG通过联合属性图方法，实现了在各种过滤类型和选择性范围内的高效过滤最近邻搜索。
摘要：
尽管过滤最近邻搜索是现代向量搜索系统中的基本任务，但现有算法的性能对查询选择性和过滤类型高度敏感。特别是现有解决方案在特定过滤类别（例如标签相等）或狭窄选择性范围内（例如低选择性预过滤）表现优异，因此不适用于需要泛化到新过滤类型和未知查询选择性的实际部署。在本文中，我们提出JAG（联合属性图），一种基于图的算法，旨在在整个选择性范围内提供稳健的性能，并支持多种过滤类型。我们的关键创新是引入属性和过滤距离，将二进制过滤约束转换为连续的导航指导。通过构建一个同时优化向量相似性和属性接近性的接近图，JAG防止导航死胡同，并使JAG能够一致地优于先前基于图的过滤最近邻搜索方法。我们在五个数据集和四种过滤类型（标签、范围、子集、布尔）上的实验结果表明，JAG在吞吐量和召回鲁棒性方面显著优于现有最先进的基线。
英文摘要：
Despite filtered nearest neighbor search being a fundamental task in modern vector search systems, the performance of existing algorithms is highly sensitive to query selectivity and filter type. In particular, existing solutions excel either at specific filter categories (e.g., label equality) or within narrow selectivity bands (e.g., pre-filtering for low selectivity) and are therefore a poor fit for practical deployments that demand generalization to new filter types and unknown query selectivities. In this paper, we propose JAG (Joint Attribute Graphs), a graph-based algorithm designed to deliver robust performance across the entire selectivity spectrum and support diverse filter types. Our key innovation is the introduction of attribute and filter distances, which transform binary filter constraints into continuous navigational guidance. By constructing a proximity graph that jointly optimizes for both vector similarity and attribute proximity, JAG prevents navigational dead-ends and allows JAG to consistently outperform prior graph-based filtered nearest neighbor search methods. Our experimental results across five datasets and four filter types (Label, Range, Subset, Boolean) demonstrate that JAG significantly outperforms existing state-of-the-art baselines in both throughput and recall robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
