short-paper

Query likelihood with negative query generation

Authors:

ChengXiang ZhaiAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1799 - 1803

https://doi.org/10.1145/2396761.2398520

Published: 29 October 2012 Publication History

Abstract

The query likelihood retrieval function has proven to be empirically effective for many retrieval tasks. From theoretical perspective, however, the justification of the standard query likelihood retrieval function requires an unrealistic assumption that ignores the generation of a "negative query" from a document. This suggests that it is a potentially non-optimal retrieval function.

In this paper, we attempt to improve the query likelihood function by bringing back the negative query generation. We propose an effective approach to estimate the probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. The proposed approach not only bridges the theoretical gap in the existing query likelihood retrieval function, but also improves retrieval effectiveness significantly with no additional computational cost.

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Cited By

Balog KRadlinski FArakelyan SPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Transparent, Scrutable and Explainable User Models for Personalized RecommendationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331211(265-274)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331211
Lv YZhai C(2015)Negative query generation: bridging the gap between query likelihood retrieval models and relevanceInformation Retrieval Journal10.1007/s10791-015-9257-z18:4(359-378)Online publication date: 6-Jun-2015
https://doi.org/10.1007/s10791-015-9257-z
Chen KWang HChen BChen H(2013)Weighted matrix factorization for spoken document retrieval2013 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2013.6639330(8530-8534)Online publication date: May-2013
https://doi.org/10.1109/ICASSP.2013.6639330

Index Terms

Query likelihood with negative query generation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

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Published: 29 October 2012

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October 29 - November 2, 2012

Hawaii, Maui, USA

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Cited By

Balog KRadlinski FArakelyan SPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Transparent, Scrutable and Explainable User Models for Personalized RecommendationProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331211(265-274)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331211
Lv YZhai C(2015)Negative query generation: bridging the gap between query likelihood retrieval models and relevanceInformation Retrieval Journal10.1007/s10791-015-9257-z18:4(359-378)Online publication date: 6-Jun-2015
https://doi.org/10.1007/s10791-015-9257-z
Chen KWang HChen BChen H(2013)Weighted matrix factorization for spoken document retrieval2013 IEEE International Conference on Acoustics, Speech and Signal Processing10.1109/ICASSP.2013.6639330(8530-8534)Online publication date: May-2013
https://doi.org/10.1109/ICASSP.2013.6639330

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