Research paper by Chen et al. examining the meaningfulness of nearest neighbor search in high-dimensional spaces. Analyzes limitations and implications for vector similarity search. Key for understanding ANN effectiveness.

Framework by Wei et al. for high-dimensional ANN search using subspace collision techniques. Offers efficiency and accuracy improvements for vector databases.

VLDB 2024 paper presenting DIDS, a fast similarity search method using double indices and double summarizations to accelerate high-dimensional vector queries.

Research paper introducing LANNS, a web-scale approximate nearest neighbor lookup system developed at Facebook (Meta). Published as an arXiv preprint in 2020, it describes techniques for serving ANN search at massive scale in production systems.

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.

Paper proposing the Satellite System Graph (NSSG) approach for high dimensional similarity search, emphasizing efficiency, scalability, and unindexed query compatibility. Published in TPAMI 2021 by Fu et al.