OHSUMED: an interactive retrieval evaluation and new large test collection for research
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Bayesian Gaussian Process Classification with the EM-EP Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Learning to rank with SoftRank and Gaussian processes
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
Multi-relational learning with Gaussian processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Fast active exploration for link-based preference learning using Gaussian processes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
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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.