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  Robust Ranking Models Using Noisy Feedback

Pölitz, C., & Schenkel, R. (2012). Robust Ranking Models Using Noisy Feedback. In Workshop "Information Retrieval Over Query Sessions" (SIR 2012) at ECIR 2012 (pp. 1-6).

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-5783-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0023-C12F-C
Genre: Conference Paper

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 Creators:
Pölitz, Christian1, Author
Schenkel, Ralf2, Author           
Affiliations:
1Cluster of Excellence "Multimodal Computing and Interaction" , Saarbrücken, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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 Abstract: Direct feedback of users of search engines by click information is naturally noisy. Ranking models that integrate such feedback in their training process must cope with this noise. In worst case such noise can lead to large variance among the results for different queries in the resulting rankings. We propose to integrate model averaging like bagging and random forest methods to reduce the variance in the ranking models. We perform an experimental study on different noise levels using a state of the art ranking model.

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Language(s): eng - English
 Dates: 2012
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 647504
Other: Local-ID: C1256DBF005F876D-63A3D0866B5A0B5DC12579E6004DCDAA-PoelitzSchenkel_SIR2012
BibTex Citekey: PoelitzSchenkel_SIR2012
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Title: 34th European Conference on IR Research
Place of Event: Barcelona, Spain
Start-/End Date: 2012-04-01 - 2012-04-01

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Title: Workshop "Information Retrieval Over Query Sessions" (SIR 2012) at ECIR 2012
  Abbreviation : ECIR 2012
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 6 Identifier: -