2021/11/11 by Rajiv Sambasivan, Sambasivan, Rajiv, Mark Burgess +13
Business, Management and Accounting · Computer Science · #Customer churn and segmentation #FOS: Computer and information sciences #Face and Expression Recognition #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.06057
arxiv created 2021/11/11 · openalex publication_date 2021/11/11 · arxiv updated 2021/11/12 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.