中南民族大学学报（自然科学版）2026，Vol.45Issue(6)：806-816,11.DOI:10.20056/j.cnki.ZNMDZK.20260609
基于扩散模型的多结构感知知识推荐
A Sequential recommendation model with structure-guided diffusion and knowledge tracing
摘要
Abstract
Most existing concept-aware sequential recommendation methods mainly rely on ID embeddings or raw textual semantics,while neglecting semantic noise in text,prerequisite-successor relations among knowledge concepts,and learners dynamically changing knowledge mastery.As a result,recommendation results often fail to match learners current cognitive states.To address these limitations,SG-DiffRec,a sequential recommendation model that integrates structure-guided diffusion with knowledge tracing is proposed.Specifically,SG-DiffRec incorporates the topological structure of a knowledge graph into the semantic generation process of diffusion models,guiding representations toward pedagogically consistent structural patterns and producing embeddings with both semantic richness and structural consistency.In addition,a deep knowledge tracing module is introduced to capture learners dynamic mastery states,while a multimodal attention mechanism adaptively fuses static knowledge attributes with dynamic learner states.Extensive experiments on three real-world educational datasets,Junyi,ASSISTments09,and ASSISTments12,demonstrate that SG-DiffRec outperforms several competitive sequential recommendation methods.关键词
序列推荐/扩散模型/知识图谱/深度知识追踪Key words
sequential recommendation/diffusion model/knowledge graph/deep knowledge tracing分类
信息技术与安全科学引用本文复制引用
宗宇航,罗昌银,李书豪,陈凌飞,张思..基于扩散模型的多结构感知知识推荐[J].中南民族大学学报（自然科学版）,2026,45(6):806-816,11.基金项目
国家自然科学基金资助项目(62577026) （62577026）
