DocumentCode :
178622
Title :
A Hierarchical Bayesian Choice Model with Visibility
Author :
Osogami, T. ; Katsuki, T.
Author_Institution :
IBM Res. - Tokyo, Tokyo, Japan
fYear :
2014
fDate :
24-28 Aug. 2014
Firstpage :
3618
Lastpage :
3623
Abstract :
We extend the standard choice model of multinomial logit model (MLM) into a hierarchical Bayesian model to simultaneously estimate the preferences of customers and the visibility of items from purchasing history. We say that an item has high visibility when customers well consider that item as a candidate before making a choice. We design two algorithms for estimating the parameters of the proposed choice model. One algorithm estimates the posterior distribution with the Gibbs sampling, and the other approximately performs the maximum a posteriori estimation. Our experimental results show that we can estimate the preferences of customers from their purchasing history without the prior knowledge of the choice set. The existing approaches to estimating the preferences of customers rely on the explicit knowledge of the choice set.
Keywords :
Bayes methods; maximum likelihood estimation; pattern recognition; visibility; Gibbs sampling; MLM; hierarchical Bayesian choice model; maximum a posteriori estimation; multinomial logit model; pattern recognition; preference estimation; purchasing history; standard choice model; visibility; Algorithm design and analysis; Approximation algorithms; Bayes methods; Estimation; History; Standards; Vectors; choice; conjoint analysis; hierarchical; logit model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location :
Stockholm
ISSN :
1051-4651
Type :
conf
DOI :
10.1109/ICPR.2014.622
Filename :
6977334
Link To Document :
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