More Web Proxy on the site http://driver.im/

research-article

Learning to Rewrite Queries

Authors:

Changsung Kang,

Yi ChangAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 1443 - 1452

https://doi.org/10.1145/2983323.2983835

Published: 24 October 2016 Publication History

Abstract

It is widely known that there exists a semantic gap between web documents and user queries and bridging this gap is crucial to advance information retrieval systems. The task of query rewriting, aiming to alter a given query to a rewrite query that can close the gap and improve information retrieval performance, has attracted increasing attention in recent years. However, the majority of existing query rewriters are not designed to boost search performance and consequently their rewrite queries could be sub-optimal. In this paper, we propose a learning to rewrite framework that consists of a candidate generating phase and a candidate ranking phase. The candidate generating phase provides us the flexibility to reuse most of existing query rewriters; while the candidate ranking phase allows us to explicitly optimize search relevance. Experimental results on a commercial search engine demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the important components of the proposed framework.

References

[1]

I. Antonellis, H. G. Molina, and C. C. Chang. Simrank+: query rewriting through link analysis of the click graph. Proceedings of the VLDB Endowment, 1(1):408--421, 2008.

Digital Library

[2]

R. Baeza-Yates, C. Hurtado, and M. Mendoza. Query recommendation using query logs in search engines. In Current Trends in Database Technology-EDBT 2004 Workshops, pages 588--596. Springer, 2005.

Digital Library

[3]

R. Baeza-Yates, B. Ribeiro-Neto, et al. Modern information retrieval, volume 463. ACM press New York, 1999.

Digital Library

[4]

R. Baeza-Yates and A. Tiberi. Extracting semantic relations from query logs. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 76--85. ACM, 2007.

Digital Library

[5]

H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 875--883. ACM, 2008.

Digital Library

[6]

H. Cui, J.-R. Wen, J.-Y. Nie, and W.-Y. Ma. Probabilistic query expansion using query logs. In Proceedings of the 11th international conference on World Wide Web, pages 325--332. ACM, 2002.

Digital Library

[7]

B. M. Fonseca, P. Golgher, B. Pôssas, B. Ribeiro-Neto, and N. Ziviani. Concept-based interactive query expansion. In Proceedings of the 14th ACM international conference on Information and knowledge management, pages 696--703. ACM, 2005.

Digital Library

[8]

J. Gao, X. He, S. Xie, and A. Ali. Learning lexicon models from search logs for query expansion. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 666--676. Association for Computational Linguistics, 2012.

Digital Library

[9]

J. Gao and J.-Y. Nie. Towards concept-based translation models using search logs for query expansion. In Proceedings of the 21st ACM international conference on Information and knowledge management, page 1. ACM, 2012.

Digital Library

[10]

A. Graves et al. Supervised sequence labelling with recurrent neural networks, volume 385. Springer, 2012.

[11]

M. Grbovic, N. Djuric, V. Radosavljevic, F. Silvestri, and N. Bhamidipati. Context-and content-aware embeddings for query rewriting in sponsored search. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 383--392. ACM, 2015.

Digital Library

[12]

C.-K. Huang, L.-F. Chien, and Y.-J. Oyang. Relevant term suggestion in interactive web search based on contextual information in query session logs. Journal of the American Society for Information Science and Technology, 54(7):638--649, 2003.

Digital Library

[13]

K. Jarvelin and J. Kek\"al\"ainen. Cumulated gain-based evaluation of ir techniques. ACM TOIS.

Digital Library

[14]

R. Jones, B. Rey, O. Madani, and W. Greiner. Generating query substitutions. In Proceedings of the 15th international conference on World Wide Web, pages 387--396. ACM, 2006.

Digital Library

[15]

T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009.

Digital Library

[16]

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111--3119, 2013.

Digital Library

[17]

T. Qin, X.-D. Zhang, M.-F. Tsai, D.-S. Wang, T.-Y. Liu, and H. Li. Query-level loss functions for information retrieval. Information Processing & Management, 44(2):838--855, 2008.

Digital Library

[18]

S. Riezler and Y. Liu. Query rewriting using monolingual statistical machine translation. Computational Linguistics, 36(3):569--582, 2010.

Digital Library

[19]

S. Riezler, Y. Liu, and A. Vasserman. Translating queries into snippets for improved query expansion. In Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1, pages 737--744. Association for Computational Linguistics, 2008.

Digital Library

[20]

A. Sordoni, Y. Bengio, H. Vahabi, C. Lioma, J. Grue Simonsen, and J.-Y. Nie. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pages 553--562. ACM, 2015.

Digital Library

[21]

I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104--3112, 2014.

Digital Library

[22]

F. Xia, T.-Y. Liu, J. Wang, W. Zhang, and H. Li. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning, pages 1192--1199. ACM, 2008.

Digital Library

[23]

J. Xu and W. B. Croft. Query expansion using local and global document analysis. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, pages 4--11. ACM, 1996.

Digital Library

[24]

W. V. Zhang and R. Jones. Comparing click logs and editorial labels for training query rewriting. In WWW 2007 Workshop on Query Log Analysis: Social And Technological Challenges, 2007.

[25]

Z. Zheng, K. Chen, G. Sun, and H. Zha. A regression framework for learning ranking functions using relative relevance judgments. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 287--294. ACM, 2007.

Digital Library

Cited By

Hwang HKim DPark JKwon YJi WFei HZheng ZFei HWei YZheng Z(2024)Bridging the Lexical Gap: Generative Text-to-Image Retrieval for Parts-of-Speech Imbalance in Vision-Language ModelsProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690089(26-34)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690089
Brahma ANagamalla SMathew JSathyanarayana J(2024)Improving search relevance in a hyperlocal food delivery using language models.Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632428(479-483)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632428
Dai AZhu ZHu HTang GLiu LXu SSerra ESpezzano F(2024)Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680109(4439-4445)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680109
Show More Cited By

Index Terms

Learning to Rewrite Queries
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Search interfaces

Recommendations

Determinacy and query rewriting for conjunctive queries and views

Answering queries using views is the problem which examines how to derive the answers to a query when we only have the answers to a set of views. Constructing rewritings is a widely studied technique to derive those answers. In this paper we consider ...
Rewriting queries with arbitrary aggregation functions using views

The problem of rewriting aggregate queries using views is studied for conjunctive queries with arbitrary aggregation functions and built-in predicates. Two types of queries over views are introduced for rewriting aggregate queries: pure candidates and ...
Queries determined by views: pack your views
PODS '07: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems

A query Q is determined by a set of views V if, whenever V (I1) = V (I2) for two database instances I1, I2 then also Q(I1) = Q(I2). Does this imply that Q can be rewritten as a query Q0 that only uses the views V?.

For first-order (FO) queries and view ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM'16

Sponsor:

CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
787
Total Downloads

Downloads (Last 12 months)82
Downloads (Last 6 weeks)13

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hwang HKim DPark JKwon YJi WFei HZheng ZFei HWei YZheng Z(2024)Bridging the Lexical Gap: Generative Text-to-Image Retrieval for Parts-of-Speech Imbalance in Vision-Language ModelsProceedings of the 2nd International Workshop on Deep Multimodal Generation and Retrieval10.1145/3689091.3690089(26-34)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3689091.3690089
Brahma ANagamalla SMathew JSathyanarayana J(2024)Improving search relevance in a hyperlocal food delivery using language models.Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD)10.1145/3632410.3632428(479-483)Online publication date: 4-Jan-2024
https://dl.acm.org/doi/10.1145/3632410.3632428
Dai AZhu ZHu HTang GLiu LXu SSerra ESpezzano F(2024)Enhancing E-Commerce Query Rewriting: A Large Language Model Approach with Domain-Specific Pre-Training and Reinforcement LearningProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680109(4439-4445)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680109
Anand AV VSetty VAnand AHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)The Surprising Effectiveness of Rankers trained on Expanded QueriesProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657938(2652-2656)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657938
Roy PSharma CGao CValegerepura KFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Deep Query Rewriting For GeocodingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615466(4801-4807)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615466
Farzana SZhou QRistoski P(2023)Knowledge Graph-Enhanced Neural Query RewritingCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587678(911-919)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587678
Galimzhanova EMuntean CNardini FPerego RRocchietti G(2023)Rewriting Conversational Utterances with Instructed Large Language Models2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00014(56-63)Online publication date: 26-Oct-2023
https://doi.org/10.1109/WI-IAT59888.2023.00014
Bulut AMahmoud A(2023)Generating Campaign Ads & Keywords for Programmatic AdvertisingIEEE Access10.1109/ACCESS.2023.326950511(43557-43565)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269505
Ning SLiu KWang CJiang SWang Q(2023)Research on Multi-channel Retrieve Mechanism Based on HeuristicData Mining and Big Data10.1007/978-981-19-8991-9_25(352-366)Online publication date: 19-Jan-2023
https://doi.org/10.1007/978-981-19-8991-9_25
Labhishetty SZhai CCrestani FPasi GGaussier E(2022)PRE: A Precision-Recall-Effort Optimization Framework for Query SimulationProceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3539813.3545136(51-60)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1145/3539813.3545136
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents