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  Learning to Rank under Tight Budget Constraints

Pölitz, C., & Schenkel, R. (2011). Learning to Rank under Tight Budget Constraints. In W.-Y. Ma, J.-Y. Nie, R. A. Baeza-Yates, T.-S. Chua, & W. B. Croft (Eds.), 34th ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1173-1174). New York, NY: ACM.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-1331-4 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0024-1332-2
Genre: Conference Paper

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 Creators:
Pölitz, Christian1, Author
Schenkel, Ralf2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 Abstract: This paper considers the problem of processing queries under budget constraints. Unlike existing work, it uses machine learning techniques not just to select features to evaluate, but also to select how many documents from an inverted list to evaluate for each feature. Experimental evaluation with TREC Terabyte queries shows that almost perfect results can be achieved with reading only 20% of all list entries.

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Language(s): eng - English
 Dates: 2011
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: PoelitzS2011
DOI: 10.1145/2009916.2010105
 Degree: -

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Title: SIGIR 2011
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Title: 34th ACM SIGIR Conference on Research and Development in Information Retrieval
  Abbreviation : SIGIR 2011
Source Genre: Proceedings
 Creator(s):
Ma, Wei-Ying1, Editor
Nie, Jian-Yun1, Editor
Baeza-Yates, Ricardo A.1, Editor
Chua, Tat-Seng1, Editor
Croft, W. Bruce1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1173 - 1174 Identifier: -