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VIRLab: a web-based virtual lab for learning and studying information retrieval models

Authors:

Hui Fang,

Hao Wu,

Peilin Yang,

ChengXiang ZhaiAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 1249 - 1250

https://doi.org/10.1145/2600428.2611178

Published: 03 July 2014 Publication History

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Abstract

In this paper, we describe VIRLab, a novel web-based virtual laboratory for Information Retrieval (IR). Unlike existing command line based IR toolkits, the VIRLab system provides a more interactive tool that enables easy implementation of retrieval functions with only a few lines of codes, simplified evaluation process over multiple data sets and parameter settings and straightforward result analysis interface through operational search engines and pair-wise comparisons. These features make VIRLab a unique and novel tool that can help teaching IR models, improving the productivity for doing IR model research, as well as promoting controlled experimental study of IR models.

References

[1]

T. G. Armstrong, A. Moffat, W. Webber, and J. Zobel. Improvements that don't add up: ad-hoc retrieval results since 1998. In Proceedings of CIKM'09, 2009.

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[2]

H. Fang, T. Tao, and C. Zhai. A formal study of information retrieval heuristics. In Proceedings of SIGIR'04, 2004.

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Cited By

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Bobić AGütl CCheong C(2020)Infret: Preliminary Findings of a Tool for Explorative Learning of Information Retrieval ConceptsCross Reality and Data Science in Engineering10.1007/978-3-030-52575-0_70(849-865)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-3-030-52575-0_70
Liu PWang QChen HXu LZhang Y(2019)Research on the Teaching Model of Experimental Virtualization in Digital Logic and Digital System Design CourseData Science10.1007/978-981-15-0121-0_27(364-374)Online publication date: 13-Sep-2019
https://doi.org/10.1007/978-981-15-0121-0_27
Yang PFang HLin J(2018)AnseriniJournal of Data and Information Quality10.1145/323957110:4(1-20)Online publication date: 29-Oct-2018
https://dl.acm.org/doi/10.1145/3239571
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Index Terms

VIRLab: a web-based virtual lab for learning and studying information retrieval models
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

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Association for Computing Machinery

New York, NY, United States

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Published: 03 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

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

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Bobić AGütl CCheong C(2020)Infret: Preliminary Findings of a Tool for Explorative Learning of Information Retrieval ConceptsCross Reality and Data Science in Engineering10.1007/978-3-030-52575-0_70(849-865)Online publication date: 20-Aug-2020
https://doi.org/10.1007/978-3-030-52575-0_70
Liu PWang QChen HXu LZhang Y(2019)Research on the Teaching Model of Experimental Virtualization in Digital Logic and Digital System Design CourseData Science10.1007/978-981-15-0121-0_27(364-374)Online publication date: 13-Sep-2019
https://doi.org/10.1007/978-981-15-0121-0_27
Yang PFang HLin J(2018)AnseriniJournal of Data and Information Quality10.1145/323957110:4(1-20)Online publication date: 29-Oct-2018
https://dl.acm.org/doi/10.1145/3239571
Geigle CLourentzou ISundaram HZhai CPolycarpou IRead JAndreou PArmoni M(2018)CLaDS: a cloud-based virtual lab for the delivery of scalable hands-on assignments for practical data science educationProceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education10.1145/3197091.3197135(176-181)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3197091.3197135
Azzopardi LCrane MFang HIngersoll GLin JMoshfeghi YScells HYang PZuccon GKando NSakai TJoho HLi Hde Vries AWhite R(2017)The Lucene for Information Access and Retrieval Research (LIARR) Workshop at SIGIR 2017Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3084374(1429-1430)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3084374
Wilhelm-Stein TKahl SEibl MKando NSakai TJoho HLi Hde Vries AWhite R(2017)Teaching the Information Retrieval Process Using a Web-Based Environment and Game MechanicsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3084143(1293-1296)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3084143
Yang PZhou MChang YZhai CFang H(2017)Towards Privacy-Preserving Evaluation for Information Retrieval Models Over Industry Data SetsInformation Retrieval Technology10.1007/978-3-319-70145-5_16(210-221)Online publication date: 8-Nov-2017
https://doi.org/10.1007/978-3-319-70145-5_16
Yang PFang HCarterette BFang HLalmas MNie J(2016)A Reproducibility Study of Information Retrieval ModelsProceedings of the 2016 ACM International Conference on the Theory of Information Retrieval10.1145/2970398.2970415(77-86)Online publication date: 12-Sep-2016
https://dl.acm.org/doi/10.1145/2970398.2970415
Kharazmi SScholer FVallet DSanderson M(2016)Examining Additivity and Weak BaselinesACM Transactions on Information Systems10.1145/288278234:4(1-18)Online publication date: 9-Jun-2016
https://dl.acm.org/doi/10.1145/2882782
Wilhelm-Stein TEibl M(2015)Teaching the IR Process Using Real Experiments Supported by Game MechanicsProceedings of the 6th International Conference on Experimental IR Meets Multilinguality, Multimodality, and Interaction - Volume 928310.1007/978-3-319-24027-5_34(312-317)Online publication date: 8-Sep-2015
https://dl.acm.org/doi/10.1007/978-3-319-24027-5_34

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Concavity in IR models

Retrieval constraints and word frequency distributions: a log-logistic model for IR

Link-based and content-based evidential information in a belief network model

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