A product-aware query auto-completion framework for e-commerce search via retrieval-augmented generation method

Query Auto-Completion (QAC) is a fundamental component of user search experience on e-commerce websites. It assists in finding userintended products, by automatically presenting search queries as users typing in the search bar. Traditional QAC systems build upon query popularity to suggest a list of potential completions, but they fall short for unforeseen search prefixes. A generative Large Language Model (LLM) can complete even unforeseen prefixes, but relevance to the product catalog of the generated suggestions is not guaranteed. To our best knowledge, there is no existing study using LLMs to generate product-aware search query completion suggestions.

This paper proposes a generative approach named "ProductRAG", to incorporate product metadata and adapt Retrieval Augmented Generation (RAG) in the development of QAC systems. Product-RAG contains two components: (1) a retrieval model that identifies top-K most relevant products from the product catalog given a user-input prefix, and (2) a generative model that offers suggestions based on both the given prefix and the retrieved product metadata. We evaluate this approach for its ability to match user-input prefixes to user-intended products, using the metrics of ROUGE scores, Mean Reciprocal Rank (MRR) and Hit Ratio (HR) in downstream product search. We observe that the proposed ProductRAG approach outperforms state-of-the-art generative models in auto-completing e-commerce search queries.