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Exploiting Result Diversification Methods for Feature Selection in Learning to Rank

  • Kaweh Djafari Naini
  • Ismail Sengor Altingovde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

Abstract

In this paper, we adopt various greedy result diversification strategies to the problem of feature selection for learning to rank. Our experimental evaluations using several standard datasets reveal that such diversification methods are quite effective in identifying the feature subsets in comparison to the baselines from the literature.

Keywords

Feature Selection Feature Selection Method Relevance Score Feature Selection Problem Modern Portfolio Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kaweh Djafari Naini
    • 1
  • Ismail Sengor Altingovde
    • 2
  1. 1.L3S Research CenterLeibniz University HannoverHannoverGermany
  2. 2.Middle East Technical UniversityAnkaraTurkey

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