research-article

About learning models with multiple query-dependent features

Authors:

Craig Macdonald,

Rodrygo L.T. Santos,

Ben HeAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 31, Issue 3

Article No.: 11, Pages 1 - 39

https://doi.org/10.1145/2493175.2493176

Published: 05 August 2013 Publication History

Abstract

Several questions remain unanswered by the existing literature concerning the deployment of query-dependent features within learning to rank. In this work, we investigate three research questions in order to empirically ascertain best practices for learning-to-rank deployments. (i) Previous work in data fusion that pre-dates learning to rank showed that while different retrieval systems could be effectively combined, the combination of multiple models within the same system was not as effective. In contrast, the existing learning-to-rank datasets (e.g., LETOR), often deploy multiple weighting models as query-dependent features within a single system, raising the question as to whether such a combination is needed. (ii) Next, we investigate whether the training of weighting model parameters, traditionally required for effective retrieval, is necessary within a learning-to-rank context. (iii) Finally, we note that existing learning-to-rank datasets use weighting model features calculated on different fields (e.g., title, content, or anchor text), even though such weighting models have been criticized in the literature. Experiments addressing these three questions are conducted on Web search datasets, using various weighting models as query-dependent and typical query-independent features, which are combined using three learning-to-rank techniques. In particular, we show and explain why multiple weighting models should be deployed as features. Moreover, we unexpectedly find that training the weighting model's parameters degrades learned model's effectiveness. Finally, we show that computing a weighting model separately for each field is less effective than more theoretically-sound field-based weighting models.

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Cited By

Aydın AArslan ADinçer B(2024)A set of novel HTML document quality features for Web information retrieval: Including applications to learning to rank for information retrievalExpert Systems with Applications10.1016/j.eswa.2024.123177246(123177)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2024.123177
Mothe JUllah M(2023)Selective Query Processing: A Risk-Sensitive Selection of Search ConfigurationsACM Transactions on Information Systems10.1145/360847442:1(1-35)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1145/3608474
Silva RGomes GAlvim MGonçalves M(2022)How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rankInformation Sciences10.1016/j.ins.2022.04.012601(90-113)Online publication date: Jul-2022
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About learning models with multiple query-dependent features
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 31, Issue 3

July 2013

202 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/2493175

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Copyright © 2013 ACM.

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Publication History

Published: 05 August 2013

Accepted: 01 February 2013

Revised: 01 September 2012

Received: 01 April 2012

Published in TOIS Volume 31, Issue 3

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Aydın AArslan ADinçer B(2024)A set of novel HTML document quality features for Web information retrieval: Including applications to learning to rank for information retrievalExpert Systems with Applications10.1016/j.eswa.2024.123177246(123177)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2024.123177
Mothe JUllah M(2023)Selective Query Processing: A Risk-Sensitive Selection of Search ConfigurationsACM Transactions on Information Systems10.1145/360847442:1(1-35)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1145/3608474
Silva RGomes GAlvim MGonçalves M(2022)How to build high quality L2R training data: Unsupervised compression-based selective sampling for learning to rankInformation Sciences10.1016/j.ins.2022.04.012601(90-113)Online publication date: Jul-2022
https://doi.org/10.1016/j.ins.2022.04.012
Mothe JUllah MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive ApproachProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482422(1335-1345)Online publication date: 26-Oct-2021
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