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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 a Eric NYBERG. Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search. In \textit{CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT}. NEW YORK: ASSOC COMPUTING MACHINERY, 2016, s.~1099-1108. ISBN~978-1-4503-4073-1. Dostupné z: https://dx.doi.org/10.1145/2983323.2983815.
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