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Query-Sensitive Embeddings

Summary: Embedding-based approximate NN for costly similarity; maps objects to a vector space to speed retrieval. Novel query-sensitive distance metric learned with the embedding adapts to the query, boosting accuracy; tested on handwritten digits and time-series, outperforming prior embeddings. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3679
Venue
SIGMOD
Year
2005
Pagerank
5.2203422e-05
Overall Rank
6,077 | 57.77%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
3,019 DSH: Data Sensitive Hashing for High-Dimensional k-NN Search 2014 SIGMOD 7.699097e-05
3,297 Approximate Embedding-Based Subsequence Matching of Time Series 2008 SIGMOD 7.2549403e-05
5,218 Neighbor-Sensitive Hashing 2016 VLDB 5.6204105e-05
6,289 Putting Context into Schema Matching 2006 VLDB 5.1226008e-05
6,651 Information Preserving XML Schema Embedding 2005 VLDB 4.9714449e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

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

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