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Showing relevant search results to the user is the primary challenge for anysearch system. Walmart e-commerce provides an omnichannel search platform toits customers to search from millions of products. This search platform takes atextual query as input and shows relevant items from the catalog. One of theprimary challenges is that this queries are complex to understand as itcontains multiple intent in many cases. This paper proposes a framework togroup search results into multiple ranked lists intending to provide betteruser intent. The framework is to create a product graph having relationsbetween product entities and utilize it to group search results into a seriesof 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 multiplestacks of "white milk", "low-fat milk", "almond milk", "flavored milk". Wemeasure the impact of our algorithm by evaluating how it improves the userexperience both in terms of search quality relevance and user behavioralsignals like Add-To-Cart.

Grouping Search Results with Product Graphs in E-commerce Platforms