Leveraging Large Language Models for Goal-driven Interactive Recommendations

We present a proof of concept application for interactive recommendations and explanations leveraging the capabilities of Large Language Models (LLMs). The application creates a highly interactive user-driven setting for recommendations giving users the possibility to explicitly tailor recommendations to their needs. Using the possibilities brought by LLMs, the application further generates convincing explanations of recommendations, aligned with the explicitly stated goals of the users. The web application continuously improves by incorporating user feedback and updating recommendations and explanations as needed.