Answers are grounded in this paper and cite their sources.
• 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.
No takes yet. Share an insight, caveat, or question.
Jin et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: