Simple and Fast Algorithm for Graph-based Filtered Approximate Nearest Neighbor Search (Full Version)

2026-07-27

AI Summary

This work addresses the challenge of efficiently supporting both vector similarity search and arbitrary attribute filtering in high-dimensional approximate nearest neighbor retrieval. The authors propose a lightweight graph-based indexing algorithm that seamlessly integrates attribute filtering into the graph traversal process, overcoming the efficiency bottlenecks of existing methods when handling unseen query vectors combined with complex attribute constraints. Experimental results on multiple real-world datasets demonstrate that the proposed approach significantly outperforms state-of-the-art techniques, achieving substantially faster query latency while maintaining high recall. The method thus offers a compelling balance among flexibility, efficiency, and scalability for hybrid vector-and-attribute search scenarios.

Abstract

It has been common to represent many objects as high-dimensional vectors due to the proliferation of machine learning-based embedding techniques. One of the most important functions for analyzing high-dimensional vectors is approximate nearest neighbor search, which, given a query vector, retrieves the vector that is approximately the most similar to the query vector. In many real-world applications, such as e-commerce, objects have not only vectors but also attributes, e.g., category, color, and brand, and they require a scenario where users can specify a query vector and a value for each attribute of interest. This problem, called filtered approximate nearest neighbor search, retrieves approximate nearest neighbors from a set of objects that have the specified attribute values. Efficiently solving this problem is challenging because it has to accept arbitrary query vectors and attribute values, which are not known in advance. Existing techniques suffer from slow search performance and difficulty in dealing with arbitrary combinations of attributes. This work overcomes these challenges and proposes a new algorithm for this problem. We conduct extensive experiments, and the results demonstrate the efficiency of our algorithm.

Problem

• filtered approximate nearest neighbor search
• high-dimensional vectors
• attribute filtering
• efficient search
• arbitrary attribute combinations

Innovation

• filtered approximate nearest neighbor search
• graph-based search
• high-dimensional vectors
• attribute filtering
• efficient retrieval