Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

Code

• github.com/arangodb/interactive_tutorials/tree/master/notebooks
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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.

Reproductions