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Conditional Ranking on Relational Data

Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski, Bernard De Baets, Conditional Ranking on Relational Data. In: José L. Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag (Eds.), Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II, 6322, 499–514, Springer, 2010.

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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BibTeX entry:

@INPROCEEDINGS{inpPaWaAiSaDe10b,
  title = {Conditional Ranking on Relational Data},
  booktitle = {Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part II},
  author = {Pahikkala, Tapio and Waegeman, Willem and Airola, Antti and Salakoski, Tapio and De Baets, Bernard},
  volume = {6322},
  editor = {Balcázar, José L. and Bonchi, Francesco and Gionis, Aristides and Sebag, Michèle},
  publisher = {Springer},
  pages = {499–514},
  year = {2010},
  keywords = {ranking, reciprocal relations, kernel methods, preference learning},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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