A shopping agent for addressing subjective product needs

In e-commerce, customers often struggle to find relevant items when their needs involve subjective properties characterized by personal or collective perception, tastes, and opinions, which are typically not captured in catalog data. This challenge is particularly pronounced in event-based scenarios like gifting, where selecting the right product involves complex subjective reasoning. Customer reviews can be a valuable source of subjective information to bridge this gap. Consequently, customers often spend significant amount of time navigating multiple products and reading numerous reviews to find suitable gifts that meet their needs. In order to reduce the effort involved, we propose an agentic approach driven by large language models to streamline this process by autonomously executing various user actions. These include computational tasks like vagueness detection and subjective product needs extraction, conversational interactions to gather missing user information, and web browsing actions that search for product details, reviews, and review images. Additionally, the agent employs generative actions to synthesize gifting ideas and explanations, helping users discover suitable products more efficiently. The proposed approach not only reduces the cognitive burden on users but also facilitates the exploration of a wider range of products. Our solution highlights the potential of autonomous agents to handle subjective queries in e-commerce, enhancing personalization, product exploration, and selection in a user-centric manner.