  Showing relevant search results to the user is the primary challenge for any
search system. Walmart e-commerce provides an omnichannel search platform to
its customers to search from millions of products. This search platform takes a
textual query as input and shows relevant items from the catalog. One of the
primary challenges is that this queries are complex to understand as it
contains multiple intent in many cases. This paper proposes a framework to
group search results into multiple ranked lists intending to provide better
user intent. The framework is to create a product graph having relations
between product entities and utilize it to group search results into a series
of stacks where each stack provides a group of items based on a precise intent.
As an example, for a query "milk," the results can be grouped into multiple
stacks of "white milk", "low-fat milk", "almond milk", "flavored milk". We
measure the impact of our algorithm by evaluating how it improves the user
experience both in terms of search quality relevance and user behavioral
signals like Add-To-Cart.
