Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Machine Learning
2021-11-12 v1
Abstract
Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity. Shopping activity is represented as a bipartite graph. This graph is refined by applying sparsity-based statistical learning methods. These methods are interpretable and reveal insights about customers' preferences as well as products driving revenue to the store.
Cite
@article{arxiv.2111.06057,
title = {Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity},
author = {Rajiv Sambasivan and Mark Burgess and Jörg Schad and Arthur Keen and Christopher Woodward and Alexander Geenen and Sachin Sharma},
journal= {arXiv preprint arXiv:2111.06057},
year = {2021}
}