Ask or Recommend: An Empirical Study on Conversational Product Search

Conversational Product Search (CPS) provides an engaging way for users to find products through effective natural language conversations. However, understanding the effect of conversational characteristics on user search performance and when to ask clarifying questions or recommend products remains unexplored. To fill the gap, we conduct an empirical study in this paper. Specifically, we developed a conversational system that allows participants to join as customers or shopping assistants, to simulate the conversational product search activity. Data collected from conversations and participant feedback indicate that: (a) CPS systems tend to ask clarifying questions early in the conversation when users express the intent of issuing a new query and chitchat, while they tend to recommend products at a later stage of conversations; asking clarifying questions early and recommending products lately can significantly improve search performance and user's satisfaction; (b) asking clarifying questions and more fine-grained search keywords positively influence search performance in terms of finding relevant products; (c) although the conversation time has a positive impact on the number of recommended products, the performance gain diminishes with longer conversation time; (d) more clarifying questions, more conversation turns, and longer system response time lead to decreased user satisfaction.