Sparse Gaussian Processes for Learning Preferences

Ehsan Abbasnejad, Edwin Bonilla, Scott Sanner

Preference learning has recently gained significant attention in the machine learning community. This is mainly due to its increasing applications in the real-world problems. In this paper, we investigate a Gaussian process framework for learning the preferences which uses Expectation Propagation (EP) as its main inference method. This framework is capable of using the information from all the users for prediction of preferences unlike the traditional approaches that only considers single-user. We furthermore extend this framework to a sparse setting and show its empirical efficiency.

Keywords: Preference Learning, Gaussian Processes, Non-parametric methods


Status: published
Type: Poster Presentation
Conference/location: NIPS, Choice Models and Preference Learning Workshop
Conference URL:
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