Bayesian Predictive Profiles with Applications to Retail Transaction Data
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 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.
Cite
Text
Cadez and Smyth. "Bayesian Predictive Profiles with Applications to Retail Transaction Data." Neural Information Processing Systems, 2001.Markdown
[Cadez and Smyth. "Bayesian Predictive Profiles with Applications to Retail Transaction Data." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/cadez2001neurips-bayesian/)BibTeX
@inproceedings{cadez2001neurips-bayesian,
title = {{Bayesian Predictive Profiles with Applications to Retail Transaction Data}},
author = {Cadez, Igor V. and Smyth, Padhraic},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {1353-1360},
url = {https://mlanthology.org/neurips/2001/cadez2001neurips-bayesian/}
}