GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMs

Previous research has used Large Language Models (LLMs) to develop
personalized Conversational Recommender Systems (CRS)
with text-based user interfaces (UIs). However, the potential of
LLMs to generate interactive graphical elements that enhance user
experience remains largely unexplored. To address this gap, we introduce
"GenUI(ne) CRS," a novel framework designed to leverage
LLMs for adaptive and interactive UIs. Our framework supports
domain-specific graphical elements such as buttons and cards, in
addition to text-based inputs. It also addresses the common LLM
issue of outdated knowledge, known as the "knowledge cut-off,"
by implementing Retrieval-Augmented Generation (RAG). To illustrate
its potential, we developed a prototype movie CRS. This work
demonstrates the feasibility of LLM-powered interactive UIs and
paves the way for future CRS research, including user experience
validation, transparent explanations, and addressing LLM biases.