Score Normalization using Logistic Regression with Expected Parameters

Robin Aly

doi:10.1007/978-3-319-06028-6_60

Score Normalization using Logistic Regression with Expected Parameters

Robin Aly

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression. Experiments on the Gov2 and CluewebA collection indicate that our method is consistently more precise in predicting the number of relevant documents in the top-n ranks compared to a state-of- the-art generative approach and another parameter estimate for logistic regression.

Original language	Undefined
Title of host publication	Proceedings of the 36th European Conference on IR Research, ECIR 2014
Place of Publication	London
Publisher	Springer
Pages	579-584
Number of pages	6
ISBN (Print)	978-3-319-06027-9
DOIs	https://doi.org/10.1007/978-3-319-06028-6_60
Publication status	Published - Apr 2014
Event	36th European Conference on Information Retrieval, ECIR 2014: (IR Resarch) - Amsterdam, Netherlands Duration: 13 Apr 2014 → 16 Apr 2014 Conference number: 36

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	8416
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	36th European Conference on Information Retrieval, ECIR 2014
Abbreviated title	ECIR
Country/Territory	Netherlands
City	Amsterdam
Period	13/04/14 → 16/04/14

Keywords

METIS-312536
EWI-25891
IR-95306

Access to Document

10.1007/978-3-319-06028-6_60

official

Cite this

@inproceedings{cbb507d8b9834e6b884080755a181971,

title = "Score Normalization using Logistic Regression with Expected Parameters",

abstract = "State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression. Experiments on the Gov2 and CluewebA collection indicate that our method is consistently more precise in predicting the number of relevant documents in the top-n ranks compared to a state-of- the-art generative approach and another parameter estimate for logistic regression.",

keywords = "METIS-312536, EWI-25891, IR-95306",

author = "Robin Aly",

note = "10.1007/978-3-319-06028-6_60 ; 36th European Conference on Information Retrieval, ECIR 2014 ; Conference date: 13-04-2014 Through 16-04-2014",

year = "2014",

month = apr,

doi = "10.1007/978-3-319-06028-6_60",

language = "Undefined",

isbn = "978-3-319-06027-9",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "579--584",

booktitle = "Proceedings of the 36th European Conference on IR Research, ECIR 2014",

address = "Germany",

}

Aly, R 2014, Score Normalization using Logistic Regression with Expected Parameters. in Proceedings of the 36th European Conference on IR Research, ECIR 2014. Lecture Notes in Computer Science, vol. 8416, Springer, London, pp. 579-584, 36th European Conference on Information Retrieval, ECIR 2014, Amsterdam, Netherlands, 13/04/14. https://doi.org/10.1007/978-3-319-06028-6_60

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N1 - Conference code: 36

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AB - State-of-the-art score normalization methods use generative models that rely on sometimes unrealistic assumptions. We propose a novel parameter estimation method for score normalization based on logistic regression. Experiments on the Gov2 and CluewebA collection indicate that our method is consistently more precise in predicting the number of relevant documents in the top-n ranks compared to a state-of- the-art generative approach and another parameter estimate for logistic regression.

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KW - EWI-25891

KW - IR-95306

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DO - 10.1007/978-3-319-06028-6_60

M3 - Conference contribution

SN - 978-3-319-06027-9

T3 - Lecture Notes in Computer Science

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EP - 584

BT - Proceedings of the 36th European Conference on IR Research, ECIR 2014

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