Score Normalization using Logistic Regression with Expected Parameters

Robin Aly

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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 languageUndefined
Title of host publicationProceedings of the 36th European Conference on IR Research, ECIR 2014
Place of PublicationLondon
PublisherSpringer
Pages579-584
Number of pages6
ISBN (Print)978-3-319-06027-9
DOIs
Publication statusPublished - Apr 2014
Event36th European Conference on Information Retrieval, ECIR 2014: (IR Resarch) - Amsterdam, Netherlands
Duration: 13 Apr 201416 Apr 2014
Conference number: 36

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
Volume8416
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference36th European Conference on Information Retrieval, ECIR 2014
Abbreviated titleECIR
CountryNetherlands
CityAmsterdam
Period13/04/1416/04/14

Keywords

  • METIS-312536
  • EWI-25891
  • IR-95306

Cite this

Aly, R. (2014). Score Normalization using Logistic Regression with Expected Parameters. In Proceedings of the 36th European Conference on IR Research, ECIR 2014 (pp. 579-584). (Lecture Notes in Computer Science; Vol. 8416). London: Springer. https://doi.org/10.1007/978-3-319-06028-6_60