GRIT: Graph-based Recall Improvement For Task-oriented E-commerce Queries

Abstract

Many e-commerce search pipelines have four stages, namely: retrieval, filtering, ranking, and personalized-reranking. The retrieval stage must be efficient and yield high recall because relevant products missed in the first stage cannot be considered in later stages. This is challenging for task-oriented queries (queries with actionable intent) where user requirements are contextually intensive and difficult to understand. To foster research in the domain of e-commerce, we created a novel benchmark for Task-oriented Queries (TQE) by using LLM, which operates over the existing ESCI product search dataset. Furthermore, we propose a novel method 'Graph-based Recall Improvement for Task-oriented queries' (GRIT) to address the most crucial first-stage recall improvement needs. GRIT leads to robust and statistically significant improvements over state-of-the-art lexical, dense, and learned-sparse baselines. Our system supports both traditional and task-oriented e-commerce queries, yielding u

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