Machine Learning

, Volume 75, Issue 1, pp 129–165

An efficient algorithm for learning to rank from preference graphs

  • Tapio Pahikkala
  • Evgeni Tsivtsivadze
  • Antti Airola
  • Jouni Järvinen
  • Jorma Boberg
Article

DOI: 10.1007/s10994-008-5097-z

Cite this article as:
Pahikkala, T., Tsivtsivadze, E., Airola, A. et al. Mach Learn (2009) 75: 129. doi:10.1007/s10994-008-5097-z

Abstract

In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown that RankRLS has many computational advantages compared to the ranking algorithms that are based on minimizing other types of costs, such as the hinge cost. In particular, we present efficient algorithms for training, parameter selection, multiple output learning, cross-validation, and large-scale learning. Circumstances under which these computational benefits make RankRLS preferable to RankSVM are considered. We evaluate RankRLS on four different types of ranking tasks using RankSVM and the standard RLS regression as the baselines. RankRLS outperforms the standard RLS regression and its performance is very similar to that of RankSVM, while RankRLS has several computational benefits over RankSVM.

Keywords

RankingPreference learningPreference graphRegularized least-squaresKernel methods
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Tapio Pahikkala
    • 1
  • Evgeni Tsivtsivadze
    • 1
  • Antti Airola
    • 1
  • Jouni Järvinen
    • 1
  • Jorma Boberg
    • 1
  1. 1.Turku Centre for Computer Science (TUCS), Department of Information TechnologyUniversity of TurkuTurkuFinland