short-paper

X-DART: Blending Dropout and Pruning for Efficient Learning to Rank

Authors:

Claudio Lucchese,

Franco Maria Nardini,

Salvatore Orlando,

Raffaele Perego,

Salvatore TraniAuthors Info & Claims

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1077 - 1080

https://doi.org/10.1145/3077136.3080725

Published: 07 August 2017 Publication History

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Abstract

In this paper we propose X-DART, a new Learning to Rank algorithm focusing on the training of robust and compact ranking models. Motivated from the observation that the last trees of MART models impact the prediction of only a few instances of the training set, we borrow from the DART algorithm the dropout strategy consisting in temporarily dropping some of the trees from the ensemble while new weak learners are trained. However, differently from this algorithm we drop permanently these trees on the basis of smart choices driven by accuracy measured on the validation set. Experiments conducted on publicly available datasets shows that X-DART outperforms DART in training models providing the same effectiveness by employing up to 40% less trees.

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Fröbe MMackenzie JMitra BNardini FPotthast MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657994(3051-3054)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657994
Bruch SMackenzie JMaistro MNardini FChen HDuh WHuang HKato MMothe JPoblete B(2023)ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591922(3456-3459)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591922
Wang KLu JLiu AZhang GXiong L(2023)Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept DriftIEEE Transactions on Cybernetics10.1109/TCYB.2021.310979653:4(2110-2123)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3109796
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Index Terms

X-DART: Blending Dropout and Pruning for Efficient Learning to Rank
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Retrieval effectiveness
    2. Retrieval models and ranking
      1. Learning to rank

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Published In

SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2017

1476 pages

ISBN:9781450350228

DOI:10.1145/3077136

General Chairs:
Noriko Kando
National Institute of Informatics
,
Tetsuya Sakai
Waseda University
,
Hideo Joho
University of Tsukuba
,
Program Chairs:
Hang Li
Huawei Noah's Ark Lab
,
Arjen P. de Vries
Radboud University
,
Ryen W. White
Microsoft Cortana

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2017

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European Commission

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SIGIR '17

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SIGIR

SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval

August 7 - 11, 2017

Tokyo, Shinjuku, Japan

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SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;

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

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Cited By

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Fröbe MMackenzie JMitra BNardini FPotthast MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)ReNeuIR at SIGIR 2024: The Third Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657994(3051-3054)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657994
Bruch SMackenzie JMaistro MNardini FChen HDuh WHuang HKato MMothe JPoblete B(2023)ReNeuIR at SIGIR 2023: The Second Workshop on Reaching Efficiency in Neural Information RetrievalProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591922(3456-3459)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591922
Wang KLu JLiu AZhang GXiong L(2023)Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept DriftIEEE Transactions on Cybernetics10.1109/TCYB.2021.310979653:4(2110-2123)Online publication date: Apr-2023
https://doi.org/10.1109/TCYB.2021.3109796
Busolin FLucchese CNardini FOrlando SPerego RTrani S(2023)Early Exit Strategies for Learning-to-Rank CascadesIEEE Access10.1109/ACCESS.2023.333108811(126691-126704)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3331088
Dato DMacAvaney SNardini FPerego RTonellotto NAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)The Istella22 DatasetProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531740(3099-3107)Online publication date: 6-Jul-2022
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Bruch SLucchese CNardini FAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)ReNeuIR: Reaching Efficiency in Neural Information RetrievalProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531704(3462-3465)Online publication date: 6-Jul-2022
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Gil-Costa VLoor FMolina RNardini FPerego RTrani S(2022)Ensemble Model Compression for Fast and Energy-Efficient Ranking on FPGAsAdvances in Information Retrieval10.1007/978-3-030-99736-6_18(260-273)Online publication date: 5-Apr-2022
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Busolin FLucchese CNardini FOrlando SPerego RTrani SDiaz FShah CSuel TCastells PJones RSakai T(2021)Learning Early Exit Strategies for Additive Ranking EnsemblesProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463088(2217-2221)Online publication date: 11-Jul-2021
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Lucchese CNardini FOrlando SPerego RTrani SHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Query-level Early Exit for Additive Learning-to-Rank EnsemblesProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401256(2033-2036)Online publication date: 25-Jul-2020
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Pereira MEtemad EPaulovich FHung CCerny TShin DBechini A(2020)Iterative learning to rank from explicit relevance feedbackProceedings of the 35th Annual ACM Symposium on Applied Computing10.1145/3341105.3374002(698-705)Online publication date: 30-Mar-2020
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