Question Suggestion for Conversational Shopping Assistants Using Product Metadata

Digital assistants have become ubiquitous in e-commerce applications,following the recent advancements in Information Retrieval (IR), NaturalLanguage Processing (NLP) and Generative Artificial Intelligence (AI). However,customers are often unsure or unaware of how to effectively converse with theseassistants to meet their shopping needs. In this work, we emphasize theimportance of providing customers a fast, easy to use, and natural way tointeract with conversational shopping assistants. We propose a framework thatemploys Large Language Models (LLMs) to automatically generate contextual,useful, answerable, fluent and diverse questions about products, via in-contextlearning and supervised fine-tuning. Recommending these questions to customersas helpful suggestions or hints to both start and continue a conversation canresult in a smoother and faster shopping experience with reduced conversationoverhead and friction. We perform extensive offline evaluations, and discuss indetail about potential customer impact, and the type, length and latency of ourgenerated product questions if incorporated into a real-world shoppingassistant.