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Abstract:
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.
