HGCRS: Hypergraph-Enhanced Generative Conversational Recommender System
Sixiao Zhang, Mingrui Liu, Cheng Long
原文摘要
Recent conversational recommender systems have begun to unify recommendation and response generation within autoregressive models, with semantic IDs providing a natural representation for generative recommendation. However, relational knowledge is typically organized around item-level representations, which do not naturally align with the compositional SIDs. This mismatch makes it difficult for unified CRSs to effectively exploit structured catalog relations. We propose HGCRS, a hypergraph-enhanced generative conversational recommender system that bridges this gap with an SID--entity hypergraph. Each item forms a hyperedge connecting its SID codes and attribute entities, and a type-aware hypergraph encoder incorporates relational information into generative item selection. HGCRS further introduces a mode--item--response protocol for jointly modeling recommendation-bearing and response-only turns. Experiments on ReDial and INSPIRED demonstrate strong recommendation performance while maintaining competitive response generation quality.
cs.IR