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A Choice Model with Infinitely Many Latent Features


Conference Paper


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.

Author(s): Görür, D. and Jäkel, F. and Rasmussen, CE.
Book Title: ICML 2006
Journal: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Pages: 361-368
Year: 2006
Month: June
Day: 0
Editors: Cohen, W. W., A. Moore
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1143844.1143890
Event Name: 23rd International Conference on Machine Learning
Event Place: Pittsburgh, PA, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PostScript


  title = {A Choice Model with Infinitely Many Latent Features},
  author = {G{\"o}r{\"u}r, D. and J{\"a}kel, F. and Rasmussen, CE.},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  pages = {361-368},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  month_numeric = {6}