山西大学学报（自然科学版）2026，Vol.49Issue(4)：557-570,14.DOI:10.13451/j.sxu.ns.2025111
层次拓扑增强的多模态推荐方法
Hierarchical Topology-enhanced Multi-modal Recommendation Method
摘要
Abstract
Recommendation systems often suffer from insufficient utilization of multi-modal information and noisy interaction data.To address these issues,this paper proposes a hierarchical topology-enhanced multi-modal recommendation method from an interac-tion denoising perspective.The method employs hierarchical anchor topology embedding,which performs stratified anchor sam-pling based on node activity levels to model global relationships,thereby mitigating over-smoothing and capturing long-range depen-dencies.Based on self-supervised learning,a learnable generator is designed to produce user multi-modal preferences and denoised interaction graphs,enabling bidirectional collaborative updating between user preferences and interaction graphs.A"graph-structure-modality"consistency constraint is incorporated to effectively suppress interaction noise.Experiments conducted on three real-world datasets demonstrate that the proposed method achieves average improvements of 2.37%and 3.27%on Recall@20 and NDCG@20 metrics,respectively,compared with 13 baseline models.关键词
自监督学习/图神经网络/交互去噪Key words
self-supervised learning/graph neural networks/interaction denoising分类
信息技术与安全科学引用本文复制引用
许钊菁,王海荣,易之航,王静..层次拓扑增强的多模态推荐方法[J].山西大学学报（自然科学版）,2026,49(4):557-570,14.基金项目
北方民族大学研究生教育质量提升项目(YJZT202424) （YJZT202424）
中国气象局旱区特色农业气象灾害监测预警与风险管理重点实验室宁夏气象防灾减灾重点实验室开放研究项目(CAMF-202502) （CAMF-202502）
北方民族大学研究生创新项目(CYX25221) （CYX25221）
