Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Igor Cadez, Padhraic Smyth
Massive transaction data sets are recorded in a routine manner in telecommunications, retail commerce, and Web site management. In this paper we address the problem of inferring predictive in- dividual proﬂles from such historical transaction data. We de- scribe a generative mixture model for count data and use an an approximate Bayesian estimation framework that eﬁectively com- bines an individual’s speciﬂc history with more general population patterns. We use a large real-world retail transaction data set to illustrate how these proﬂles consistently outperform non-mixture and non-Bayesian techniques in predicting customer behavior in out-of-sample data.