Conditional ranking on relational data

Authors:
Tapio Pahikkala;Willem Waegeman;Antti Airola;Tapio Salakoski;Bernard De Baets
Affiliations:
University of Turku and Turku Centre for Computer Science, Turku, Finland;Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Ghent, Belgium;University of Turku and Turku Centre for Computer Science, Turku, Finland;University of Turku and Turku Centre for Computer Science, Turku, Finland;Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Ghent, Belgium
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Year:
2010

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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.