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Abstract:
Massive transaction data sets are recorded in a routine manner
in telecommunications, retail commerce, and Web site management.
In this paper we address the problems of inferring predictive
individual profiles from such historical transaction data. We
describe a generative mixture model for count data and use an
approximate Bayesian estimation framework that effectively
combines and individual's specific history with more general
population patterns. We use a large real-world retail transaction
data set to illustrate how these profiles consistently outperform
non-mixture and non-Bayesian techniques in predicting customer
behavior in out-of-sample data.
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