A Choice Model with Infinitely Many Latent Features

Görür, D; Jäkel, F; Rasmussen, CE

doi:10.1145/1143844.1143890

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A Choice Model with Infinitely Many Latent Features

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Görür, D
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Jäkel, F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rasmussen, CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://dl.acm.org/citation.cfm?doid=1143844.1143890
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ICML-2006-Goeruer.pdf
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引用

Görür, D., Jäkel, F., & Rasmussen, C. (2006). A Choice Model with Infinitely Many Latent Features. In W., Cohen, & A., Moore (Eds.), ICML '06: Proceedings of the 23rd International Conference on Machine Learning (pp. 361-368). New York, NY, USA: ACM Press.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-D12F-9

要旨

Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by binary features and associated weights. For instance, when choosing which mobile phone to buy the features to consider may be: long lasting battery, color screen, etc. Existing methods for inferring the parameters of the model assume pre-specified features. However, the features that lead to the observed choices are not always known. Here, we present a non-parametric Bayesian model to infer the features of the options and the corresponding weights from choice data. We use the Indian buffet process (IBP) as a prior over the features. Inference using Markov chain Monte Carlo (MCMC) in conjugate IBP models has been previously described. The main contribution of this paper is an MCMC algorithm for the EBA model that can also be used in inference for other non-conjugate IBP models---this may broaden the use of IBP priors considerably.