解决景点推荐中图文缺失问题，通过动态消息传递补全信息并提升推荐效果。
HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation
- 构建多智能体超图，动态传递语义消息完成缺失模态补全。
- 在60%模态缺失下，平均提升NDCG@20达8.2%，超越15个基线模型。
- 缓存最终表示，实现无大模型调用的高效在线推荐，适合真实场景部署。
基于位置的社交网络中，下一景点推荐依赖于描述场所语义的文本和视觉内容，但现实服务中此类内容常不完整。缺失模态削弱了景点表征，降低排序时的语义证据，导致用户-景点高阶交互建模不可靠。我们提出HyperAgent4POI，利用动态语义消息传递（DSMP）在每层超图中完成模态补全与软关联优化。持久化节点代理使用冻结的Llama主干，并通过角色适配器生成节点到超边的消息。由这些消息形成的语义超边模式引导软关联评分与模态补全。最终节点表征被缓存，支持无需调用大模型的在线排序。在三个真实世界LBSN数据集上的实验表明，无论模态缺失率如何，其排名表现持续优于15个基线。在60%模态缺失率下，平均相比最强基线提升NDCG@20达8.2%。
原文摘要 · Abstract (English)
Next Point-of-Interest (POI) recommendation benefits from textual and visual content that describes venue semantics, yet such content is often incomplete in real-world services. Missing modalities weaken POI representations and reduce the semantic evidence available for ranking. The resulting representations also provide unreliable evidence for modeling higher-order user--POI interactions. We propose HyperAgent4POI, which uses Dynamic Semantic Message Passing (DSMP) to perform modality completion and soft incidence refinement within each hypergraph layer. Persistent node agents share a frozen Llama backbone and use role-specific adapters to produce node-to-hyperedge messages. Semantic hyperedge motifs formed from these messages guide soft incidence scoring and modality completion. Final node representations are cached for online ranking without LLM calls. Experiments on three real-world LBSN datasets show consistent ranking gains over 15 baselines across modality-missing rates, while cached inference provides practical online efficiency. Under a 60% modality-missing rate, HyperAgent4POI improves NDCG@20 over the strongest baseline by 8.2% on average across the three datasets.
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