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Non-parametric Bayesian Learning of User Preferences: Elicitation, Sparsification and Beyond

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Bayesian approaches to preference elicitation (PE) are particularly attractive due to their ability to explicitly model uncertainty in users’ latent utility functions. However, previous approaches to Bayesian PE have ignored the important problem of generalizing from previous users to an unseen user in order to reduce the elicitation burden on new users. In this talk, I will present an approach that addresses this deficiency by introducing a Gaussian Process (GP) prior over users’ latent utility functions on the joint space of user and item features. The hyper-parameters of this GP are learned on a set of preferences of previous users and this information is leveraged to aid in the elicitation process for a new user. This approach provides a flexible model of a multi-user utility function, facilitates an efficient value of information (VOI) heuristic query selection strategy, and provides a principled way to incorporate the elicitations of multiple users back into the model. I will show the effectiveness of this method in comparison to previous work on a real dataset of user preferences over sushi types. Additionally, I will present recent work on loss-sensitive sparsification approaches to scale up GP-based preference models.

This talk is part of the Machine Learning @ CUED series.

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