  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.
