Name
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 individual profiles from such historical transaction data. We describe a generative mixture model for count data and use an an approximate Bayesian estimation framework that effectively combines an 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. |
Year | Venue | Keywords |
|---|---|---|
2001 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2 | count data,customer behavior,mixture model,transaction data |
Field | DocType | Volume |
Data mining,Population,Computer science,Consumer behaviour,Bayesian average,Artificial intelligence,Count data,Bayes estimator,Transaction data,Machine learning,Mixture model,Bayesian probability | Conference | 14 |
ISSN | Citations | PageRank |
1049-5258 | 0 | 0.34 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor V. Cadez | 1 | 263 | 29.24 |
| Padhraic Smyth | 2 | 7148 | 1451.38 |