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Frontiers of Computer Science

, Volume 6, Issue 6, pp 647–659 | Cite as

Listwise approaches based on feature ranking discovery

  • Yongqing Wang
  • Wenji Mao
  • Daniel Zeng
  • Fen Xia
Research Article

Abstract

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BLFeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

Keywords

learning to rank listwise approach feature’s ranking discovery 

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yongqing Wang
    • 1
  • Wenji Mao
    • 2
  • Daniel Zeng
    • 2
    • 3
  • Fen Xia
    • 4
  1. 1.Commercial Products Development DepartmentAlibaba Inc.HangzhouChina
  2. 2.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
  3. 3.Department of Management Information SystemsUniversity of ArizonaTucsonUSA
  4. 4.Union Research and Development DepartmentBaidu Inc.BeijingChina

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