Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems (ChatCRS)
Large language models, even the most advanced ones, still struggle in conversational recommendation: they find it difficult to generate grounded, recommendation-oriented responses, or to proactively steer the conversation through different dialogue goals rather than passively responding to the user. As a result, LLM-based conversational recommenders often produce generic suggestions that are not well grounded in domain knowledge, and fail to guide the conversation strategically towards a successful recommendation.
We introduce ChatCRS, a framework that decomposes conversational recommendation into two specialised components: a knowledge retrieval agent, which uses a tool-augmented approach to reason over external knowledge bases, and a goal-planning agent, which predicts the next dialogue goal to keep the conversation moving productively towards a recommendation. By combining explicit knowledge grounding with goal-oriented planning, ChatCRS improves informativeness by 17% and proactivity by 27%, and boosts recommendation accuracy by up to 10x on multi-goal recommendation datasets.