Article

A combined component approach for finding collection-adapted ranking functions based on genetic programming

Authors:

Humberto Mossri de Almeida,

Marcos André Gonçalves,

Pável CaladoAuthors Info & Claims

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 399 - 406

https://doi.org/10.1145/1277741.1277810

Published: 23 July 2007 Publication History

Abstract

In this paper, we propose a new method to discover collection-adapted ranking functions based on Genetic Programming (GP). Our Combined Component Approach (CCA)is based on the combination of several term-weighting components (i.e.,term frequency, collection frequency, normalization) extracted from well-known ranking functions. In contrast to related work, the GP terminals in our CCA are not based on simple statistical information of a document collection, but on meaningful, effective, and proven components. Experimental results show that our approach was able to outper form standard TF-IDF, BM25 and another GP-based approach in two different collections. CCA obtained improvements in mean average precision up to 40.87% for the TREC-8 collection, and 24.85% for the WBR99 collection (a large Brazilian Web collection), over the baseline functions. The CCA evolution process also was able to reduce the overtraining, commonly found in machine learning methods, especially genetic programming, and to converge faster than the other GP-based approach used for comparison.

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Cited By

Keyhanipour A(2024)Learning to rank through graph-based feature fusion using fuzzy integral operatorsApplied Intelligence10.1007/s10489-024-05755-w54:22(11914-11932)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05755-w
Khan HNasir SNasim KShabbir DMahmood A(2021)Twitter trends: A ranking algorithm analysis on real time dataExpert Systems with Applications10.1016/j.eswa.2020.113990164(113990)Online publication date: Feb-2021
https://doi.org/10.1016/j.eswa.2020.113990
Moreo AEsuli ASebastiani F(2020)Learning to Weight for Text ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288344632:2(302-316)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TKDE.2018.2883446
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Index Terms

A combined component approach for finding collection-adapted ranking functions based on genetic programming
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

July 2007

946 pages

ISBN:9781595935977

DOI:10.1145/1277741

General Chairs:
Wessel Kraaij
TNO, The Netherlands
,
Arjen P. de Vries
CWI, The Netherlands
,
Program Chairs:
Charles L. A. Clarke
University of Waterloo, Canada
,
Norbert Fuhr
University of Duisburg-Essen, Germany
,
Noriko Kando
National Institute of Informatics, Japan

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 July 2007

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SIGIR07: The 30th Annual International SIGIR Conference

July 23 - 27, 2007

Amsterdam, The Netherlands

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

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Cited By

Keyhanipour A(2024)Learning to rank through graph-based feature fusion using fuzzy integral operatorsApplied Intelligence10.1007/s10489-024-05755-w54:22(11914-11932)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s10489-024-05755-w
Khan HNasir SNasim KShabbir DMahmood A(2021)Twitter trends: A ranking algorithm analysis on real time dataExpert Systems with Applications10.1016/j.eswa.2020.113990164(113990)Online publication date: Feb-2021
https://doi.org/10.1016/j.eswa.2020.113990
Moreo AEsuli ASebastiani F(2020)Learning to Weight for Text ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.288344632:2(302-316)Online publication date: 1-Feb-2020
https://doi.org/10.1109/TKDE.2018.2883446
Silva SDe Moura ECalado PDa Silva A(2020)Effective Lightweight Learning-to-Rank Method Using Unified Term ImpactsIEEE Access10.1109/ACCESS.2020.29869438(70420-70437)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2986943
Boughanem MAkermi IPasi GAbdulahhad K(2020)Information Retrieval and Artificial IntelligenceA Guided Tour of Artificial Intelligence Research10.1007/978-3-030-06170-8_5(147-180)Online publication date: 8-May-2020
https://doi.org/10.1007/978-3-030-06170-8_5
Baeza-Yates RCuzzocrea ACrea DBianco GHung CPapadopoulos G(2019)An effective and efficient algorithm for ranking web documents via genetic programmingProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297385(1065-1072)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297385
Yoon JKim JJang B(2018)DiTeX: Disease-related topic extraction system through internet-based sourcesPLOS ONE10.1371/journal.pone.020193313:8(e0201933)Online publication date: 3-Aug-2018
https://doi.org/10.1371/journal.pone.0201933
Kim MKim ILee MJang B(2018)Worldwide emerging disease-related information extraction system from news dataProceedings of the 16th ACM Conference on Embedded Networked Sensor Systems10.1145/3274783.3275168(331-332)Online publication date: 4-Nov-2018
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Cordova Neira MRibeiro Mendes Junior PRocha ADa Silva Torres R(2018)Data-Fusion Techniques for Open-Set Recognition ProblemsIEEE Access10.1109/ACCESS.2018.28242406(21242-21265)Online publication date: 2018
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Jang BYoon J(2018)Characteristics Analysis of Data From News and Social Network ServicesIEEE Access10.1109/ACCESS.2018.28187926(18061-18073)Online publication date: 2018
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