Key points are not available for this paper at this time.
• Review-driven structural induction alleviates sparsity in conversational recommendation. • LLM-distilled review semantics are grounded as entities to densify hypergraph structures. • Item-centric hypergraph learning supports joint recommendation and response generation. • Two-stage training stabilizes structure learning under sparse conversational supervision.
Jin et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: