DB-LSH — Locality-Sensitive Hashing with Query-based Dynamic Bucketing

SIGIR 2025 paper proposing a locality-sensitive indexing approach for graph-based approximate nearest neighbor search, combining LSH principles with graph structure for improved search accuracy.

VLDB 2021 paper introducing MP-RW-LSH, an efficient multi-probe locality-sensitive hashing solution for A1-norm (Manhattan distance) approximate nearest neighbor search.

VLDB 2022 paper introducing PM-LSH, an in-memory locality-sensitive hashing framework for high-dimensional approximate nearest neighbor and closest pair search with strong accuracy guarantees.

VLDB 2015 paper introducing QALSH, a query-aware locality-sensitive hashing scheme that improves retrieval accuracy by dynamically adjusting hash functions based on query characteristics.

VLDB 2023 paper proposing LSH-APG, a method combining locality-sensitive hashing with adaptive proximity graphs for efficient index construction and approximate nearest neighbor search in high-dimensional spaces.

Multi-Vector Retrieval Algorithm that reduces multi-vector similarity search to single-vector similarity search via Fixed Dimensional Encodings. Achieves 10% improved recall with 90% lower latency compared to existing approaches.