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@inproceedings{1377704,
author = {Boytsov, Leonid and Novák, David and Malkov, Yury and Nyberg, Eric},
address = {NEW YORK},
booktitle = {CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT},
doi = {http://dx.doi.org/10.1145/2983323.2983815},
keywords = {k-NN search; IBM Model 1; non-metric spaces; LSH},
howpublished = {paměťový nosič},
language = {eng},
location = {NEW YORK},
isbn = {978-1-4503-4073-1},
pages = {1099-1108},
publisher = {ASSOC COMPUTING MACHINERY},
title = {Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search},
year = {2016}
}
TY - JOUR
ID - 1377704
AU - Boytsov, Leonid - Novák, David - Malkov, Yury - Nyberg, Eric
PY - 2016
TI - Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
PB - ASSOC COMPUTING MACHINERY
CY - NEW YORK
SN - 9781450340731
KW - k-NN search
KW - IBM Model 1
KW - non-metric spaces
KW - LSH
N2 - Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online.(1)
ER -
BOYTSOV, Leonid, David NOVÁK, Yury MALKOV and Eric NYBERG. Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search. Online. In \textit{CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT}. NEW YORK: ASSOC COMPUTING MACHINERY, 2016. p.~1099-1108. ISBN~978-1-4503-4073-1. Available from: https://dx.doi.org/10.1145/2983323.2983815. [citováno 2024-04-23]
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