short-paper

Personalized Query Suggestion Diversification

Authors:

Maarten de RijkeAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 817 - 820

https://doi.org/10.1145/3077136.3080652

Published: 07 August 2017 Publication History

Abstract

Query suggestions help users refine their queries after they input an initial query. We consider the task of generating query suggestions that are personalized and diversified. We propose a personalized query suggestion diversification model (PQSD), where a user's long-term search behavior is injected into a basic greedy query suggestion diversification model (G-QSD) that considers a user's search context in their current session. Query aspects are identified through clicked documents based on the Open Directory Project (ODP). We quantify the improvement of PQSD over a state-of-the-art baseline using the AOL query log and show that it beats the baseline in terms of metrics used in query suggestion ranking and diversification. The experimental results show that PQSD achieves the best performance when only queries with clicked documents are taken as search context rather than all queries.

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Cited By

Baek JChandrasekaran NCucerzan SHerring AJauhar SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Knowledge-Augmented Large Language Models for Personalized Contextual Query SuggestionProceedings of the ACM on Web Conference 202410.1145/3589334.3645404(3355-3366)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645404
Khan TRashid UKhan A(2024)End-to-end pseudo relevance feedback based vertical web search queries recommendationMultimedia Tools and Applications10.1007/s11042-024-18559-4Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11042-024-18559-4
Wang SDou ZLiu JZhu QWen J(2023)Personalized and Diversified: Ranking Search Results in an Integrated WayACM Transactions on Information Systems10.1145/363198942:3(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631989
Show More Cited By

Index Terms

Personalized Query Suggestion Diversification
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

Copyright © 2017 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Baek JChandrasekaran NCucerzan SHerring AJauhar SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Knowledge-Augmented Large Language Models for Personalized Contextual Query SuggestionProceedings of the ACM on Web Conference 202410.1145/3589334.3645404(3355-3366)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645404
Khan TRashid UKhan A(2024)End-to-end pseudo relevance feedback based vertical web search queries recommendationMultimedia Tools and Applications10.1007/s11042-024-18559-4Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11042-024-18559-4
Wang SDou ZLiu JZhu QWen J(2023)Personalized and Diversified: Ranking Search Results in an Integrated WayACM Transactions on Information Systems10.1145/363198942:3(1-25)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1145/3631989
Kim SNa EKim S(2022)Developing a Meta-Suggestion Engine for Search QueriesIEEE Access10.1109/ACCESS.2022.318609610(68513-68520)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3186096
Chen JMao JLiu YZhang FZhang MMa S(2021)Towards a Better Understanding of Query Reformulation Behavior in Web SearchProceedings of the Web Conference 202110.1145/3442381.3450127(743-755)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450127
Cai GLi XDai QWang GDong ZZhang CHe XShang L(2021)Dual Sequence Transformer for Query-based Interactive Recommendation2021 22nd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM52706.2021.00030(139-144)Online publication date: Jun-2021
https://doi.org/10.1109/MDM52706.2021.00030
Miao YQin JHu SDong YIshikawa YOnizuka M(2020)NGNC: A Flexible and Efficient Framework for Error-Tolerant Query AutocompletionSoftware Foundations for Data Interoperability and Large Scale Graph Data Analytics10.1007/978-3-030-61133-0_8(101-115)Online publication date: 6-Nov-2020
https://doi.org/10.1007/978-3-030-61133-0_8
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Fails JPera MAnuyah OKennington CWright KBigirimana WFails J(2019)Query Formulation Assistance for KidsProceedings of the 18th ACM International Conference on Interaction Design and Children10.1145/3311927.3323131(109-120)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1145/3311927.3323131
Chen WCai FChen HDe Rijke M(2019)Personalized query suggestion diversification in information retrievalFrontiers of Computer Science10.1007/s11704-018-7283-x14:3Online publication date: 19-Dec-2019
https://doi.org/10.1007/s11704-018-7283-x
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