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.

翻译：虽然近似最近邻（ANN）搜索已被广泛研究，但旨在检索大量最近邻的大规模k ANN查询在实际应用中虽频繁出现，却仍未被充分探索。现有ANN方法在处理此类查询时性能显著下降。本研究首先探究了基于量化的ANN索引性能衰退的原因：（1）现有top-k收集器的低效性导致候选维护开销过大，（2）量化方法的剪枝效果减弱引发高代价的重排序过程。为此，我们提出新型的基于桶的结果收集器（BBC），以提升现有基于量化的ANN索引在大规模k ANN查询中的效率。BBC引入两个关键组件：（1）基于桶的结果缓冲区，根据候选对象与查询的距离将其组织到不同桶中。该设计降低了排序成本并改善了缓存效率，从而实现候选超集的高性能维护和最终top-k结果的轻量级选择；（2）两种针对不同类型量化方法定制的重排序算法，通过减少待重排候选对象数量或缓存缺失次数来加速重排序过程。在真实数据集上的大量实验表明，对于大规模k ANN查询，当recall@k = 0.95时，BBC可将现有基于量化的ANN方法加速最高达3.8倍。