Bridging the Gap Between Information Seeking and Product Search Systems: Q&A Recommendation for E-commerce

TL;DR

This paper explores integrating conversational QA with product search in e-commerce, proposing Q&A pair recommendations to enhance consumer decision-making using large language models.

Contribution

It introduces a framework for recommending relevant Q&A pairs during product search, bridging information seeking and product discovery with LLMs.

Findings

Identifies key requirements for effective Q&A recommendations.

Discusses challenges and potential solutions in integrating QA with product search.

Highlights open problems and future research directions.

Abstract

Consumers on a shopping mission often leverage both product search and information seeking systems, such as web search engines and Question Answering (QA) systems, in an iterative process to improve their understanding of available products and reach a purchase decision. While product search is useful for shoppers to find the actual products meeting their requirements in the catalog, information seeking systems can be utilized to answer any questions they may have to refine those requirements. The recent success of Large Language Models (LLMs) has opened up an opportunity to bridge the gap between the two tasks to help customers achieve their goals quickly and effectively by integrating conversational QA within product search. In this paper, we propose to recommend users Question-Answer (Q&A) pairs that are relevant to their product search and can help them make a purchase decision. We discuss the different aspects of the problem including the requirements and characteristics of the Q&A pairs, their generation, and the optimization of the Q&A recommendation task. We highlight the challenges, open problems, and suggested solutions to encourage future research in this emerging area.