  Conversational recommendation (ConvRec) systems must understand rich and
diverse natural language (NL) expressions of user preferences and intents,
often communicated in an indirect manner (e.g., "I'm watching my weight"). Such
complex utterances make retrieving relevant items challenging, especially if
only using often incomplete or out-of-date metadata. Fortunately, many domains
feature rich item reviews that cover standard metadata categories and offer
complex opinions that might match a user's interests (e.g., "classy joint for a
date"). However, only recently have large language models (LLMs) let us unlock
the commonsense connections between user preference utterances and complex
language in user-generated reviews. Further, LLMs enable novel paradigms for
semi-structured dialogue state tracking, complex intent and preference
understanding, and generating recommendations, explanations, and question
answers. We thus introduce a novel technology RA-Rec, a Retrieval-Augmented,
LLM-driven dialogue state tracking system for ConvRec, showcased with a video,
open source GitHub repository, and interactive Google Colab notebook.
