BBC: Improving Large-k Approximate Nearest Neighbor Search with a Bucket-based Result Collector

2026-04-02

AI Summary

This work addresses the significant performance degradation of existing quantization-based approximate nearest neighbor (ANN) methods under large-k queries, primarily caused by inefficient top-k result collection and costly re-ranking. To overcome these limitations, the authors propose a Bucket-based Collector (BBC), which organizes candidate vectors into distance-based buckets to reduce both candidate maintenance overhead and final sorting costs. Additionally, they introduce two efficient re-ranking algorithms tailored to different quantization schemes, effectively minimizing the number of items requiring re-ranking and mitigating cache misses. Experimental results demonstrate that, at a recall@k of 0.95, BBC accelerates state-of-the-art quantization-based ANN methods by up to 3.8×.

Technology Category

Machine Learning: Learning Preferences or RankingsData Mining & Knowledge Management: Data CompressionSearch and Optimization: Distributed Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingGraph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphsUser Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Although Approximate Nearest Neighbor (ANN) search has been extensively studied, large-k ANN queries that aim to retrieve a large number of nearest neighbors remain underexplored, despite their numerous real-world applications. Existing ANN methods face significant performance degradation for such queries. In this work, we first investigate the reasons for the performance degradation of quantization-based ANN indexes: (1) the inefficiency of existing top-k collectors, which incurs significant overhead in candidate maintenance, and (2) the reduced pruning effectiveness of quantization methods, which leads to a costly re-ranking process. To address this, we propose a novel bucket-based result collector (BBC) to enhance the efficiency of existing quantization-based ANN indexes for large-k ANN queries. BBC introduces two key components: (1) a bucket-based result buffer that organizes candidates into buckets by their distances to the query. This design reduces ranking costs and improves cache efficiency, enabling high performance maintenance of a candidate superset and a lightweight final selection of top-k results. (2) two re-ranking algorithms tailored for different types of quantization methods, which accelerate their re-ranking process by reducing either the number of candidate objects to be re-ranked or cache misses. Extensive experiments on real-world datasets demonstrate that BBC accelerates existing quantization-based ANN methods by up to 3.8x at recall@k = 0.95 for large-k ANN queries.

Problem

• large-k ANN
• Approximate Nearest Neighbor
• quantization-based indexes
• top-k retrieval
• performance degradation

Innovation

• approximate nearest neighbor
• large-k retrieval
• quantization-based indexing
• bucket-based collector
• re-ranking optimization