Knowledge Interaction and Diffusion Augmentation for Knowledge Graph Recommendation
DOI:
https://doi.org/10.13052/jwe1540-9589.2472Keywords:
Knowledge Graph(KG), recommendation, knowledge interaction, diffusion model.Abstract
Knowledge graphs (KGs) provide rich semantic and structured knowledge to address data sparsity in traditional recommendation systems, yet efficiently leveraging KGs for better performance remains challenging. This paper proposes KIDRec, a knowledge graph recommendation framework integrating knowledge interaction and diffusion-augmented learning. This framework captures deep KG semantics via high-order entity interactions and uses a diffusion model to generate robust knowledge representations through “noise injection and denoising” on collaborative user embeddings and item embeddings. It then incorporates an adaptive contrastive learning module with hard negative sample enhancement and a dynamic temperature coefficient. Experiments on Book-Crossing, Last.FM, and MovieLens20m show KIDRec outperforms baselines in AUC (0.782, 0.877, 0.979) and F1 (0.682, 0.796, 0.932), with relative AUC improvements of 0.031, 0.032, and 0.002, respectively, while alleviating data sparsity.
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