Abstract: Graph-based collaborative filtering recommendation techniques have gained significant attention for their ability to efficiently process large-scale interaction data.However,the effectiveness of these techniques is limited by the sparsity of data in real-world scenarios.Recent research has started to apply contrastive learning to graph collaborative filtering to enhance its performance.Nonetheless,existing methods often construct contrastive pairs through random sampling,failing to fully explore the potential of contrastive learning in recommendation systems.To address these issues,this paper introduces a collaborative filtering model based on FeatureNet Contrastive Learning(FCL).The model establishes a node feature similarity matrix by computing the cosine similarity between feature vectors and applying a probabilistic normalization strategy.Using contrastive learning to perform influence analysis on the node feature similarity matrix,the model captures high-order connectivity between nodes,particularly demonstrating significant effectiveness in handling datasets with high sparsity.Extensive experiments conducted on multiple datasets prove the effectiveness of the proposed model.

Key words: Recommendation algorithm, Contrastive learning, Collaborative filtering, Graph neural networks, Data augmentation