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Conference Paper

Learning to Rank under Tight Budget Constraints

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons45380

Schenkel,  Ralf
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0024-1331-4
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.