LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrieval

Summary: LIDER: a clustering-based hierarchical learned index for high-dimensional dense passage retrieval that reduces embeddings to 1D keys via extended SortingKeys-LSH + key re-scaling and an adapted RMI core. Outperforms ANN baselines (~1.2x speed) with higher retrieval quality and better speed–quality trade-offs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13016
Venue: VLDB
Year: 2023
Pagerank: 4.6387029e-05
Overall Rank: 7,832 | 45.52%
DOI: 10.14778/3565816.3565819

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,690	Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment	2024	SIGMOD	8.293714e-05
8,076	Accelerating String-key Learned Index Structures via Memoization-based Incremental Training	2024	VLDB	4.5917398e-05
8,439	Accelerating Graph Indexing for ANNS on Modern CPUs	2025	SIGMOD	4.5128946e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
102	The Case for Learned Index Structures	2018	SIGMOD	0.00049545203
400	Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search	2007	VLDB	0.0002427237
801	SageDB: A Learned Database System	2019	CIDR	0.00016505496
1,229	SK-LSH : An Efficient Index Structure for Approximate Nearest Neighbor Search	2014	VLDB	0.00013157271
1,478	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00011762542

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