A hierarchical recurrent encoder-decoder for generative context-aware query suggestion

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Standard

A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. / Sordoni, Alessandro; Bengio, Yoshua; Vahabi, Hossein; Lioma, Christina; Simonsen, Jakob Grue; Nie, Jian-Yun.

CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, 2015. p. 553-562.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Harvard

Sordoni, A, Bengio, Y, Vahabi, H, Lioma, C, Simonsen, JG & Nie, J-Y 2015, A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. in CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, pp. 553-562, CIKM 2015, Melbourne, Australia, 19/10/2015. https://doi.org/10.1145/2806416.2806493

APA

Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Simonsen, J. G., & Nie, J-Y. (2015). A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (pp. 553-562). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806493

Vancouver

Sordoni A, Bengio Y, Vahabi H, Lioma C, Simonsen JG, Nie J-Y. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery. 2015. p. 553-562 https://doi.org/10.1145/2806416.2806493

Author

Sordoni, Alessandro ; Bengio, Yoshua ; Vahabi, Hossein ; Lioma, Christina ; Simonsen, Jakob Grue ; Nie, Jian-Yun. / A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, 2015. pp. 553-562

Bibtex

@inproceedings{0a1d45c89ae8431bbeabc72ea020daf5,
title = "A hierarchical recurrent encoder-decoder for generative context-aware query suggestion",
abstract = "Users may strive to formulate an adequate textual query fortheir information need. Search engines assist the users bypresenting query suggestions. To preserve the original searchintent, suggestions should be context-aware and account forthe previous queries issued by the user. Achieving contextawareness is challenging due to data sparsity. We presenta probabilistic suggestion model that is able to account forsequences of previous queries of arbitrary lengths. Our novelhierarchical recurrent encoder-decoder architecture allowsthe model to be sensitive to the order of queries in the contextwhile avoiding data sparsity. Additionally, our modelcan suggest for rare, or long-tail, queries. The produced suggestionsare synthetic and are sampled one word at a time,using computationally cheap decoding techniques. This is incontrast to current synthetic suggestion models relying uponmachine learning pipelines and hand-engineered feature sets.Results show that it outperforms existing context-aware approachesin a next query prediction setting. In addition toquery suggestion, our model is general enough to be used ina variety of other applications.",
author = "Alessandro Sordoni and Yoshua Bengio and Hossein Vahabi and Christina Lioma and Simonsen, {Jakob Grue} and Jian-Yun Nie",
year = "2015",
doi = "10.1145/2806416.2806493",
language = "English",
pages = "553--562",
booktitle = "CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management",
publisher = "Association for Computing Machinery",
note = "CIKM 2015 : ACM International Conference on Information and Knowledge Management ; Conference date: 19-10-2015 Through 23-10-2015",

}

RIS

TY - GEN

T1 - A hierarchical recurrent encoder-decoder for generative context-aware query suggestion

AU - Sordoni, Alessandro

AU - Bengio, Yoshua

AU - Vahabi, Hossein

AU - Lioma, Christina

AU - Simonsen, Jakob Grue

AU - Nie, Jian-Yun

PY - 2015

Y1 - 2015

N2 - Users may strive to formulate an adequate textual query fortheir information need. Search engines assist the users bypresenting query suggestions. To preserve the original searchintent, suggestions should be context-aware and account forthe previous queries issued by the user. Achieving contextawareness is challenging due to data sparsity. We presenta probabilistic suggestion model that is able to account forsequences of previous queries of arbitrary lengths. Our novelhierarchical recurrent encoder-decoder architecture allowsthe model to be sensitive to the order of queries in the contextwhile avoiding data sparsity. Additionally, our modelcan suggest for rare, or long-tail, queries. The produced suggestionsare synthetic and are sampled one word at a time,using computationally cheap decoding techniques. This is incontrast to current synthetic suggestion models relying uponmachine learning pipelines and hand-engineered feature sets.Results show that it outperforms existing context-aware approachesin a next query prediction setting. In addition toquery suggestion, our model is general enough to be used ina variety of other applications.

AB - Users may strive to formulate an adequate textual query fortheir information need. Search engines assist the users bypresenting query suggestions. To preserve the original searchintent, suggestions should be context-aware and account forthe previous queries issued by the user. Achieving contextawareness is challenging due to data sparsity. We presenta probabilistic suggestion model that is able to account forsequences of previous queries of arbitrary lengths. Our novelhierarchical recurrent encoder-decoder architecture allowsthe model to be sensitive to the order of queries in the contextwhile avoiding data sparsity. Additionally, our modelcan suggest for rare, or long-tail, queries. The produced suggestionsare synthetic and are sampled one word at a time,using computationally cheap decoding techniques. This is incontrast to current synthetic suggestion models relying uponmachine learning pipelines and hand-engineered feature sets.Results show that it outperforms existing context-aware approachesin a next query prediction setting. In addition toquery suggestion, our model is general enough to be used ina variety of other applications.

U2 - 10.1145/2806416.2806493

DO - 10.1145/2806416.2806493

M3 - Article in proceedings

SP - 553

EP - 562

BT - CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

PB - Association for Computing Machinery

T2 - CIKM 2015

Y2 - 19 October 2015 through 23 October 2015

ER -

ID: 159746271