BBC: Improving Large-k Approximate Nearest Neighbor Search with a Bucket-based Result Collector
Summary: BBC targets the overlooked large-k regime in quantization-based ANN, where top-k maintenance and weak pruning dominate cost. Its distance-bucketed result buffer improves cache efficiency and candidate handling, while specialized re-ranking yields up to 3.8× speedups at 0.95 recall. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Ziqi Yin (Nanyang Technological University)
- 2. Gao Cong (Nanyang Technological University)
- 3. Kai Zeng (Huawei)
- 4. Jinwei Zhu (Huawei)
- 5. Bin Cui (Peking University)
BibTeX Citation
@article{yin_vldb26,
title = {{BBC: Improving Large-k Approximate Nearest Neighbor Search with a Bucket-based Result Collector}},
author = {Yin, Ziqi and Cong, Gao and Zeng, Kai and Zhu, Jinwei and Cui, Bin},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {11},
pages = {3468--3482},
doi = {10.14778/3836663.3836702},
url = {https://doi.org/10.14778/3836663.3836702},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,829 | RNSG: A Range-Aware Graph Index for Efficient Range-Filtered Approximate Nearest Neighbor Search | 2026 | VLDB | 4.9793485e-05 |
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