CUED Publications database

Collaborative Gaussian processes for preference learning

Houlsby, N and Hernández-Lobato, JM and Huszár, F and Ghahramani, Z (2012) Collaborative Gaussian processes for preference learning. Advances in Neural Information Processing Systems, 3. pp. 2096-2104. ISSN 1049-5258

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We present a new model based on Gaussian processes (GPs) for learning pairwise preferences expressed by multiple users. Inference is simplified by using a preference kernel for GPs which allows us to combine supervised GP learning of user preferences with unsupervised dimensionality reduction for multi-user systems. The model not only exploits collaborative information from the shared structure in user behavior, but may also incorporate user features if they are available. Approximate inference is implemented using a combination of expectation propagation and variational Bayes. Finally, we present an efficient active learning strategy for querying preferences. The proposed technique performs favorably on real-world data against state-of-the-art multi-user preference learning algorithms.

Item Type: Article
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 18 Aug 2020 13:28