Related papers: Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

URL: http://arxiv.org/abs/2111.06057v1
Date: Thu, 11 Nov 2021 05:36:39 GMT
Title: Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Authors: Rajiv Sambasivan, Mark Burgess, J\"org Schad, Arthur Keen, Christopher Woodward, Alexander Geenen, Sachin Sharma
Abstract summary: 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.
Score: 54.26540039514418
License: http://creativecommons.org/licenses/by/4.0/
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.

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