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)

Abstract

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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References

  1. 1.
    Fisher, L.: Rock, Paper, Scissors: Game Theory in Everyday Life. Basic Books (2008)Google Scholar
  2. 2.
    Pahikkala, T., Waegeman, W., Tsivtsivadze, E., Salakoski, T., De Baets, B.: Learning intransitive reciprocal relations with kernel methods. European Journal of Operational Research 206(3), 676–685 (2010)MATHCrossRefGoogle Scholar
  3. 3.
    Yamanishi, Y., Vert, J., Kanehisa, M.: Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 20, 1363–1370 (2004)CrossRefGoogle Scholar
  4. 4.
    Weston, J., Eliseeff, A., Zhou, D., Leslie, C., Noble, W.S.: Protein ranking: from local to global structure in the protein similarity network. Proceedings of the National Academy of Sciences of the United States of America 101(17), 6559–6563 (2004)CrossRefGoogle Scholar
  5. 5.
    Freund, Y., Yier, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933–969 (2003)CrossRefGoogle Scholar
  6. 6.
    Waegeman, W., De Baets, B., Boullart, L.: Learning layered ranking functions with structured support vector machines. Neural Networks 21(10), 1511–1523 (2008)CrossRefGoogle Scholar
  7. 7.
    Fürnkranz, J., Hüllermeier, E., Vanderlooy, S.: Binary decomposition methods for multipartite ranking. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS, vol. 5781, pp. 359–374. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Joachims, T.: Optimizing search engines using clickthrough data. In: Hand, D., Keim, D., Ng, R. (eds.) Proceedings of the 8th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 133–142. ACM Press, New York (2002)CrossRefGoogle Scholar
  9. 9.
    Pahikkala, T., Tsivtsivadze, E., Airola, A., Boberg, J., Salakoski, T.: Learning to rank with pairwise regularized least-squares. In: Joachims, T., Li, H., Liu, T.Y., Zhai, C. (eds.) SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, pp. 27–33 (2007)Google Scholar
  10. 10.
    Pahikkala, T., Tsivtsivadze, E., Airola, A., Järvinen, J., Boberg, J.: An efficient algorithm for learning to rank from preference graphs. Machine Learning 75(1), 129–165 (2009)CrossRefGoogle Scholar
  11. 11.
    Yang, Y., Bansal, N., Dakka, W., Ipeirotis, P., Koudas, N., Papadias, D.: Query by document. In: Baeza-Yates, R.A., Boldi, P., Ribeiro-Neto, B.A., Cambazoglu, B.B. (eds.) Proceedings of the 2nd International Conference on Web Search and Data Mining, pp. 34–43. ACM Press, New York (2009)CrossRefGoogle Scholar
  12. 12.
    Schölkopf, B., Smola, A.: Learning with Kernels, Support Vector Machines, Regularisation, Optimization and Beyond. MIT Press, Cambridge (2002)Google Scholar
  13. 13.
    Suykens, J., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore (2002)MATHCrossRefGoogle Scholar
  14. 14.
    Ben-Hur, A., Noble, W.: Kernel methods for predicting protein-protein interactions. Bioinformatics 21(suppl. 1), 38–46 (2005)CrossRefGoogle Scholar
  15. 15.
    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: De Raedt, L., Wrobel, S. (eds.) Proceedings of the 22nd international conference on Machine learning. ACM International Conference Proceeding Series, vol. 119, pp. 89–96. ACM Press, New York (2005)CrossRefGoogle Scholar
  16. 16.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: UAI 2009: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press (2009)Google Scholar
  17. 17.
    Abadir, M., Magnus, J.: Matrix Algebra. Cambridge University Press, Cambridge (2005)MATHGoogle Scholar
  18. 18.
    Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Mathematics and Its Applications, vol. 375. Kluwer Academic Publishers, Dordrecht (1996)MATHGoogle Scholar
  19. 19.
    van der Vorst, H.A.: BI-CGSTAB: a fast and smoothly converging variant of BI-CG for the solution of nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing 13(2), 631–644 (1992)MATHCrossRefGoogle Scholar
  20. 20.
    Joachims, T.: Training linear SVMs in linear time. In: Eliassi-Rad, T., Ungar, L.H., Craven, M., Gunopulos, D. (eds.) Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 217–226. ACM Press, New York (2006)CrossRefGoogle Scholar
  21. 21.
    Agarwal, S.: Ranking on graph data. In: Cohen, W.W., Moore, A. (eds.) Proceedings of the 23rd International Conference on Machine Learning. ACM International Conference Proceeding Series, vol. 148, pp. 25–32. ACM Press, New York (2006)CrossRefGoogle Scholar

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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