Conditional Ranking on Relational Data

  • Tapio Pahikkala
  • Willem Waegeman
  • Antti Airola
  • Tapio Salakoski
  • Bernard De Baets
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6322)


In domains like bioinformatics, information retrieval and social network analysis, one can find learning tasks where the goal consists of inferring a ranking of objects, conditioned on a particular target object. We present a general kernel framework for learning conditional rankings from various types of relational data, where rankings can be conditioned on unseen data objects. Conditional ranking from symmetric or reciprocal relations can in this framework be treated as two important special cases. Furthermore, we propose an efficient algorithm for conditional ranking by optimizing a squared ranking loss function. Experiments on synthetic and real-world data illustrate that such an approach delivers state-of-the-art performance in terms of predictive power and computational complexity. Moreover, we also show empirically that incorporating domain knowledge in the model about the underlying relations can improve the generalization performance.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tapio Pahikkala
    • 1
  • Willem Waegeman
    • 2
  • Antti Airola
    • 1
  • Tapio Salakoski
    • 1
  • Bernard De Baets
    • 2
  1. 1.University of Turku and Turku Centre for Computer ScienceTurkuFinland
  2. 2.Department of Applied Mathematics, Biometrics and Process ControlGhent UniversityGhentBelgium

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