2021/11/11 by Rajiv Sambasivan, Sambasivan, Rajiv, Mark S. Burgess +11
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)
paper · pdf · doi:10.48550/arxiv.2111.06057
openalex publication_date 2021/11/11 · 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.