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Learning Preferences with Hidden Common Cause Relations

Kristian Kersting; Zhao Xu
In: Wray L. Buntine; Marko Grobelnik; Dunja Mladenic; John Shawe-Taylor (Hrsg.). Machine Learning and Knowledge Discovery in Databases, European Conference. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD-2009), September 7-11, Bled, Slovenia, Pages 676-691, Lecture Notes in Computer Science, Vol. 5781, Springer, 2009.


Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic inference. For many entities encountered in real-world applications, however, there are complex relations between them. In this paper, we present a preference model which incorporates information on relations among entities. Specifically, we propose a probabilistic relational kernel model for preference learning based on Silva et al.’s mixed graph Gaussian processes: a new prior distribution, enhanced with relational graph kernels, is proposed to capture the correlations between preferences. Empirical analysis on the LETOR datasets demonstrates that relational information can improve the performance of preference learning.

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