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.

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Cited By

Zha DBhat ZLai KYang FJiang ZZhong SHu X(2025)Data-centric Artificial Intelligence: A SurveyACM Computing Surveys10.1145/371111857:5(1-42)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3711118
Rocchietti GRulli CMaria Nardini FIoana Muntean CPerego RFrieder O(2025)ChatGPT Versus Modest Large Language Models: An Extensive Study on Benefits and Drawbacks for Conversational SearchIEEE Access10.1109/ACCESS.2025.352974113(15253-15271)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3529741
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
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Index Terms

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

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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.

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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

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

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Cited By

Zha DBhat ZLai KYang FJiang ZZhong SHu X(2025)Data-centric Artificial Intelligence: A SurveyACM Computing Surveys10.1145/371111857:5(1-42)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3711118
Rocchietti GRulli CMaria Nardini FIoana Muntean CPerego RFrieder O(2025)ChatGPT Versus Modest Large Language Models: An Extensive Study on Benefits and Drawbacks for Conversational SearchIEEE Access10.1109/ACCESS.2025.352974113(15253-15271)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3529741
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
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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
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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
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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
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Bulut AMahmoud A(2023)Generating Campaign Ads & Keywords for Programmatic AdvertisingIEEE Access10.1109/ACCESS.2023.326950511(43557-43565)Online publication date: 2023
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