Collaborative Recommendation with Knowledge Graph Enhancement and Contrastive Learning

Recommendation systems aim to provide personalized recommendations by analyzing user preferences. However, they encounter significant challenges of data sparsity and distribution skewness, which can impair their accuracy. To address these issues, we propose a collaborative recommendation approach integrating Knowledge Graph Enhancement and Contrastive Learning(KGECL). In this approach, the Knowledge Graph (KG) enhancement module first generates structurally diverse subgraphs via random perturbations and then calculates their structural stability scores to guide the generation of the user-item interaction(U-I) graph. The U-I graph enhancement module generates contrastive views from perturbed graphs using Variational Graph Autoencoders (VGAE)to alleviate the impact of skewed data distribution and sparsity. Finally, the enhanced KG is concatenated with the U-I graph to jointly construct embeddings for both users and items. Experimental results on three public datasets (MIND, yelp2018, and amazon-book) demonstrate that KGECL outperforms baseline models in terms of Recall and Normalized Discounted Cumulative Gain (NDCG), achieving the desired improvements.