Universal Approximation Functions for Fast Learning to Rank-Reference-Cited by-同舟云学术

Universal Approximation Functions for Fast Learning to Rank

Published:2018-06-27 Issue: Volume: Page:
ISSN:
Container-title:The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
language:
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Author:

Cohen Daniel¹,Foley John¹,Zamani Hamed¹,Allan James¹,Croft W. Bruce¹

Affiliation:

1. University of Massachusetts Amherst, Amherst, MA, USA

Funder

University of Southern California

Air Force Research Laboratory

NFS

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3209978.3210137

Reference26 articles.

1. Nima Asadi and Jimmy Lin . 2013. Training efficient tree-based models for document ranking (ECIR). 146--157. 10.1007/978-3-642-36973-5_13 Nima Asadi and Jimmy Lin . 2013. Training efficient tree-based models for document ranking (ECIR). 146--157. 10.1007/978-3-642-36973-5_13

2. Michael Bendersky W Bruce Croft and Yanlei Diao . 2011. Quality-biased ranking of web documents (WSDM). 95--104. 10.1145/1935826.1935849 Michael Bendersky W Bruce Croft and Yanlei Diao . 2011. Quality-biased ranking of web documents (WSDM). 95--104. 10.1145/1935826.1935849

3. Microsoft Research Blog . 2015. RankNet: A ranking retrospective. https://www.microsoft.com/en-us/research/blog/ranknet-a-ranking-retrospective/. (2015). Microsoft Research Blog . 2015. RankNet: A ranking retrospective. https://www.microsoft.com/en-us/research/blog/ranknet-a-ranking-retrospective/. (2015).

4. Quality versus efficiency in document scoring with learning-to-rank models

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