Amer-Yahia S. , Berti-Equille Laure, Chibah A. (2021). A framework for statistically-sound customer segment search. In :
2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA). Piscataway : IEEE, 1-10. International Conference on Data Science and Advanced Analytics (DSAA), 8., Porto (PRT), 2021/10/06-09. ISBN 978-1-6654-2100-3.
Titre du document
A framework for statistically-sound customer segment search
2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)
Source
Piscataway : IEEE, 2021,
1-10 ISBN 978-1-6654-2100-3
Colloque
International Conference on Data Science and Advanced Analytics (DSAA), 8., Porto (PRT), 2021/10/06-09
We develop S4, a Statistically-Sound Segment Search framework that combines principled data partitioning and sound statistical testing to verify common hypotheses in retail data and return interpretable customer data segments. Our framework accommodates one-sample, two-sample, and multiple-sample testing, to provide various aggregations and comparisons of customer transactions. To control the proportion of false discoveries in multiple hypothesis testing, we enforce an FDR-controlling procedure and formulate a unified optimization problem that returns customer data segments that satisfy the test for a given significance level, maximize coverage of the input data, and are within a risk capital. We develop a greedy algorithm to explore different data partitions and test multiple hypotheses in a sound manner. Our extensive experiments on four retail data sets examine the interaction between significance, risk and coverage, and demonstrate the expressivity, usefulness, and scalability of S4 in practice.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Informatique [122]
