Bayesian Predictive Profiles With Applications to Retail Transaction Data

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

Bibtex Metadata Paper


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.