poster

Revisiting the Divergence Minimization Feedback Model

Authors:

ChengXiang ZhaiAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1863 - 1866

https://doi.org/10.1145/2661829.2661900

Published: 03 November 2014 Publication History

Abstract

Pseudo-relevance feedback (PRF) has proven to be an effective strategy for improving retrieval accuracy. In this paper, we revisit a PRF method based on statistical language models, namely the divergence minimization model (DMM). DMM not only has apparently sound theoretical foundation, but also has been shown to satisfy most of the retrieval constraints. However, it turns out to perform surprisingly poorly in many previous experiments. We investigate the cause, and reveal that DMM inappropriately tackles the entropy of the feedback model, which generates highly skewed feedback model. To address this problem, we propose a maximum-entropy divergence minimization model (MEDMM) by introducing an entropy term to regularize DMM. Our experiments on various TREC collections demonstrate that MEDMM not only works much better than DMM, but also outperforms several other state of the art PRF methods, especially on web collections. Moreover, unlike existing PRF models that have to be combined with the original query to perform well, MEDMM can work effectively even without being combined with the original query.

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Cited By

Le DTran APham THuynh T(2024)A Survey of Model Compression and Its Feedback Mechanism in Federated LearningProceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval10.1145/3643488.3660293(37-42)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643488.3660293
Zhu WZhang XYe LZhai Q(2023)Query Context Expansion for Open-Domain Question AnsweringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360349822:8(1-21)Online publication date: 23-Aug-2023
https://dl.acm.org/doi/10.1145/3603498
Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Show More Cited By

Index Terms

Revisiting the Divergence Minimization Feedback Model
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval models and ranking

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cover image ACM Conferences

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

Copyright © 2014 ACM.

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Published: 03 November 2014

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Division of Computer and Network Systems

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CIKM '14

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

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CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

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

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Cited By

Le DTran APham THuynh T(2024)A Survey of Model Compression and Its Feedback Mechanism in Federated LearningProceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval10.1145/3643488.3660293(37-42)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643488.3660293
Zhu WZhang XYe LZhai Q(2023)Query Context Expansion for Open-Domain Question AnsweringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/360349822:8(1-21)Online publication date: 23-Aug-2023
https://dl.acm.org/doi/10.1145/3603498
Sheetrit ERaiber FKurland OYoshioka MKiseleva JAliannejadi M(2023)Entity-Based Relevance Feedback for Document RetrievalProceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3578337.3605128(177-187)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.1145/3578337.3605128
Li HMourad AZhuang SKoopman BZuccon G(2023)Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and PitfallsACM Transactions on Information Systems10.1145/357072441:3(1-40)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3570724
Zhu YPang LLan YShen HCheng XAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)LoLProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532017(825-836)Online publication date: 6-Jul-2022
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Li HZhuang SMourad AMa XLin JZuccon G(2022)Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility StudyAdvances in Information Retrieval10.1007/978-3-030-99736-6_40(599-612)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99736-6_40
Ma HHou JZhu CZhang WTang RLai JZhu JHe XYu Y(2021)QA4PRF: A Question Answering Based Framework for Pseudo Relevance FeedbackIEEE Access10.1109/ACCESS.2021.31186009(139303-139314)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3118600
Arampatzis APeikos GSymeonidis S(2021)Pseudo relevance feedback optimizationInformation Retrieval10.1007/s10791-021-09393-524:4-5(269-297)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s10791-021-09393-5
Yu HDai ZCallan J(2021)PGT: Pseudo Relevance Feedback Using a Graph-Based TransformerAdvances in Information Retrieval10.1007/978-3-030-72240-1_46(440-447)Online publication date: 28-Mar-2021
https://dl.acm.org/doi/10.1007/978-3-030-72240-1_46
Montazeralghaem AZamani HAllan JHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)A Reinforcement Learning Framework for Relevance FeedbackProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401099(59-68)Online publication date: 25-Jul-2020
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