Knowledge graph-based recommendation with knowledge noise reduction and data augmentation

In the field of recommendation algorithms, Knowledge Graphs are often utilized as supplementary information to enhance recommendation accuracy. However, while applying Knowledge Graphs enriches recommendation information, it also introduces potentially misleading effects due to Knowledge Graph noise. To address these challenges, we propose a method to achieve Knowledge Graph Noise Reduction and Knowledge Perception Enhancement through positive contrast learning. The method employs Fourier Transform, Inverse Transform, and Convolution Optimization Computation techniques to transform and analyze Knowledge Graph triplet information in the frequency domain. The process filters and reduces noise through Convolution Optimization Computation by integrating frequency domain feature information, eliminating misleading relational information that cannot effectively infer user preferences. Subsequently, Positive Contrastive Learning enhances the acquired ternary information and improves the applicability of the information to recognize user preference information accurately. The proposed method reduces noise and enhances knowledge perception, strengthens the application of strong relationships, reduces the impact of weak relationships, and improves recommendation accuracy by utilizing frequency-domain features. The KG-CFCL_RippleNet and KG-CFCL_MKR models validate the effectiveness of this optimization method, and significant improvements are achieved in the area of book, music, and movie recommendations compared to existing models. Experiments demonstrate the advantages of KG-CFCL in terms of noise reduction, Knowledge Perception Enhancement, and data optimization, improving the interpretability of the models.