Knowledge-aware Multi-Level Diffusion Contrastive Learning for Recommendation

Knowledge Graphs (KGs) cover abundant factual information and semantic associations between items. They are often used to enhance the representation ability of models in the field of recommendation systems, and provide key support for improving recommendation performance. However, not all information in KGs has the same relevance and value to the target recommendation task. KGs themselves generally have significant inherent sparsity. Some connection information obtained from KGs may also introduce extra noise. Moreover, user behavior collaborative signals and KGs semantic signals are often modeled and processed separately. In this paper, we propose a new knowledge graph-based recommendation framework called KMDCL. To fully explore the semantic value of KGs and improve their adaptability to recommendation tasks, we build a knowledge graph completion strategy using a locally deployed large language model (LLM) . We also propose a knowledge graph denoising method based on diffusion models to accurately filter out low-confidence noisy relations. These two methods are combined to purify and enhance the information of KGs. On this basis, we combine the optimized KG with the user-item interaction graph to build a multi-view representation space for items. We also introduce a contrastive learning mechanism to enhance the semantic discrimination and expression ability of item embeddings. Our extensive experiments on three public datasets show that KMDCL has achieved significant performance improvement compared with a variety of baseline models. Experimental results show that this method can effectively alleviate the challenges brought by data sparsity and knowledge graph noise, and enhance the learned representation to improve the recommendation performance.