Article

Context-sensitive information retrieval using implicit feedback

Authors:

ChengXiang ZhaiAuthors Info & Claims

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 43 - 50

https://doi.org/10.1145/1076034.1076045

Published: 15 August 2005 Publication History

Abstract

A major limitation of most existing retrieval models and systems is that the retrieval decision is made based solely on the query and document collection; information about the actual user and search context is largely ignored. In this paper, we study how to exploit implicit feedback information, including previous queries and clickthrough information, to improve retrieval accuracy in an interactive information retrieval setting. We propose several context-sensitive retrieval algorithms based on statistical language models to combine the preceding queries and clicked document summaries with the current query for better ranking of documents. We use the TREC AP data to create a test collection with search context information, and quantitatively evaluate our models using this test set. Experiment results show that using implicit feedback, especially the clicked document summaries, can improve retrieval performance substantially.

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Cited By

Robinson KGarvin MEglash RRobert LGuzdial MBennett A(2024)Making exploratory search engines using qualitative case studies: a mixed method implementation using interviews with Detroit ArtisansJournal of Integrated Global STEM10.1515/jigs-2024-0007Online publication date: 15-Nov-2024
https://doi.org/10.1515/jigs-2024-0007
Zhu HHuang JRudinac SKanoulas EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language ModelsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658032(978-987)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658032
Liu CCheung CXu MZhang ZSu MFan M(2024)Toward Facilitating Search in VR With the Assistance of Vision Large Language ModelsProceedings of the 30th ACM Symposium on Virtual Reality Software and Technology10.1145/3641825.3687742(1-14)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3641825.3687742
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Index Terms

Context-sensitive information retrieval using implicit feedback
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

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Published In

cover image ACM Conferences

SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval

August 2005

708 pages

ISBN:1595930345

DOI:10.1145/1076034

General Chairs:
Ricardo Baeza-Yates
University of Chile, Chile
,
Nivio Ziviani
Federal University of Minas Gerais, Brazil
,
Program Chairs:
Gary Marchionini
University of North Carolina, USA
,
Alistair Moffat
University of Melbourne, Australia
,
John Tait
University of Sunderland, UK

Copyright © 2005 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: 15 August 2005

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SIGIR05: The 28th ACM/SIGIR International Symposium on Information Retrieval 2005

August 15 - 19, 2005

Salvador, Brazil

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

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Cited By

Robinson KGarvin MEglash RRobert LGuzdial MBennett A(2024)Making exploratory search engines using qualitative case studies: a mixed method implementation using interviews with Detroit ArtisansJournal of Integrated Global STEM10.1515/jigs-2024-0007Online publication date: 15-Nov-2024
https://doi.org/10.1515/jigs-2024-0007
Zhu HHuang JRudinac SKanoulas EGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language ModelsProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658032(978-987)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3652583.3658032
Liu CCheung CXu MZhang ZSu MFan M(2024)Toward Facilitating Search in VR With the Assistance of Vision Large Language ModelsProceedings of the 30th ACM Symposium on Virtual Reality Software and Technology10.1145/3641825.3687742(1-14)Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1145/3641825.3687742
Wu STu QZhong MLiu HXu JGu JYan RSerra ESpezzano F(2024)Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document RankingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679661(2564-2574)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679661
Salemi AZamani HHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657733(741-751)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657733
Wu STu QLiu HXu JLiu ZZhang GWang RChen XYan RChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Unify Graph Learning with Text: Unleashing LLM Potentials for Session SearchProceedings of the ACM Web Conference 202410.1145/3589334.3645574(1509-1518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645574
Chen HDou ZZhu YWen J(2024)Query-Oriented Data Augmentation for Session SearchIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.341913136:11(6877-6888)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3419131
Liu JWang MWang JWang YChu X(2024)Long short-term search session-based document re-ranking modelKnowledge and Information Systems10.1007/s10115-024-02205-4Online publication date: 9-Sep-2024
https://doi.org/10.1007/s10115-024-02205-4
Shah CWhite RThomas PMitra BSarkar SBelkin N(2023)Taking Search to TaskProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578288(1-13)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578288
Ma SChen CMao JTian QJiang XChen HDuh WHuang HKato MMothe JPoblete B(2023)Session Search with Pre-trained Graph Classification ModelProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591766(953-962)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591766
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