Learning Preferences with Hidden Common Cause Relations

author: Kristian Kersting, Fraunhofer IAIS
published: Oct. 20, 2009,   recorded: September 2009,   views: 2649

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

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.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: