Retrieving and Ranking Relevant Products from Boolean Natural Language Queries

E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such form. These natural language retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially- matched relevant results that could be of interested to the customers. This undesirable effect costs e-commerce vendors to lose sells on their merchandise. In solving this problem, we propose a product retrieval system, called QuePR. The advantages of QuePR are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. QuePR is unique, easy to use, and scalable to all product categories. We have conducted different performance analysis to verify the effectiveness of QuePR and compared QuePR with other ranking and retrieval systems. The empirical results show that QuePR outperforms others and is efficient.