DPEKG:Knowledge Graph Recommendation Model Based on Diffusion and Adaptive Denoising Enhancement

Wu Zheng; Luo Weiqun

doi:10.3969/j.issn.1004-3403.2025.02.011

Abstract：In recent years,knowledge graphs have provided an effective tool for capturing semantic associations be-tween users and items in recommendation systems.However,existing researches mainly focuses on simple direct rela-tionships and fail to fully utilize higher-order semantic associations,and when dealing with graph noise,it lacks fine-grained manipulation of local structures,and redundant information is not thoroughly removed,which ultimately af-fects recommendation accuracy.To solve these problems,this paper proposes a Diffusion and Adaptive Post-En-hancement Knowledge Graph(DPEKG)recommendation model:first,the model mines multi-hop paths in the user-item interaction graph through the path optimization module to construct an enhanced item-user interaction view;sec-ond,the model introduces a diffusion denoising mechanism to gradually diffuse and eliminate the global noise in the knowledge graph,so that the noise-filtered graph can more accurately reflect the real user-item association;subse-quently,through the adaptive denoising enhancement module,the model dynamically weights and scores the denoised graph,and retains the nodes and edges most relevant to users'interests and items'preferences;finally,the model performs multi-perspective embedding alignment of the user-item interaction view and the denoised knowledge graph view through contrastive learning to optimize recommendation effects.The experimental results show that the recom-mendation performance of DPEKG on multiple public datasets significantly outperforms that of existing methods,verifying its effectiveness in dealing with complex relationships and noise interference.