FAVOR: Efficient Filter-Agnostic Vector ANNS Based on Selectivity-Aware Exclusion Distances

2026-05-08

AI Summary

This work addresses the challenge that existing hybrid vector-attribute query methods struggle to simultaneously achieve efficiency, generality, and graph connectivity under low-selectivity conditions. The authors propose an efficient, filter-oblivious approximate nearest neighbor search framework that dynamically optimizes query paths by unifying selectivity estimation with search execution. Key innovations include a selectivity-aware exclusion distance mechanism to reshape distance distributions, a selectivity-driven dynamic routing strategy, and an inline filtering algorithm built upon HNSW. Experimental results demonstrate that, at Recall@10 = 95%, the proposed method achieves 1.3–5× higher queries per second (QPS) than state-of-the-art approaches on real-world datasets, while matching the performance of specialized solutions under specific filtering constraints.

Abstract

Modern retrieval systems increasingly require integrating approximate nearest neighbor search (ANNS) with complex attribute filtering to handle hybrid queries in applications such as recommendation systems and retrieval-augmented generation (RAG). While HNSW-based inline-filtering methods show promise, existing approaches struggle to deliver high throughput under low-selectivity scenarios while balancing search efficiency, filtering generality, and index connectivity. To address these challenges, we propose FAVOR, an efficient filter-agnostic vector ANNS that supports arbitrary filtering conditions while maintaining stable performance across varying selectivity levels. FAVOR introduces three novel features: (1) an integrated architecture that unifies selectivity estimation and filtered ANNS execution, providing a cohesive solution for hybrid vector-attribute queries; (2) a HNSW-based inline-filtering algorithm that introduces an exclusion distance mechanism to dynamically reshape the vector distance distribution, pushing non-target vectors away from the query while promoting valid candidates toward the query, thus improving search efficiency without compromising generality or graph connectivity; and (3) a selectivity-driven search selector that estimates query selectivity and dynamically routes queries between a pre-filtering brute-force algorithm for low-selectivity cases and an optimized HNSW-based search algorithm for other scenarios, ensuring consistent performance. Extensive experiments on real-world datasets demonstrate that FAVOR achieves a 1.3-5$\times$ higher QPS at $Recall@10 = 95\%$ compared to state-of-the-art methods for arbitrary filtering conditions, while maintaining competitive performance even against tailored solutions in some filtering conditions.

Problem

• approximate nearest neighbor search
• attribute filtering
• hybrid queries
• low selectivity
• filter-agnostic

Innovation

• filter-agnostic
• exclusion distance
• selectivity-aware
• hybrid vector-attribute search
• HNSW-based inline filtering