Grouping Search Results with Product Graphs in E-commerce Platforms

Abstract

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.

References in corpus (7)

• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Convolutional Matrix Completion
• Variational Graph Auto-Encoders
• Representation Learning for Attributed Multiplex Heterogeneous Network
• STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
• Fairness-Aware Explainable Recommendation over Knowledge Graphs
• Ordinal Non-negative Matrix Factorization for Recommendation