Abstract: With the explosive growth of Internet data,recommender systems have become crucial for addressing the problem of information overload.Graph contrastive learning-based recommendation models have demonstrated significant advantages in enhancing model performance by improving user-item interaction graphs.Although these models have achieved some success,most existing methods rely on perturbing graph structures for data augmentation.However,this approach struggles to preserve the inherent semantic structure and is vulnerable to noise interference.To further enhance the performance of recommendation models,this paper proposes a novel self-attention-based graph contrastive learning recommendation algorithm(AttGCL).On the one hand,the integrated self-attention mechanism strengthens the connections between users and items,allowing the model to capture user preferences and individual differences more accurately.On the other hand,the ICL loss function effectively controls the importance of positive and negative samples,leading to better alignment between global and local representations.This method retains the essential semantics of user-item interactions,enabling the model to reflect user preferences more accurately and improve recommendation effectiveness.Experimental results on three real-world datasets show that AttGCL significantly outperforms existing graph contrastive learning methods in terms of performance,demonstrating its advantages in efficiency and robustness.

Key words: Recommendation system, Graph contrastive learning, Self-attention mechanism, Graph convolutional network, Con-trastive learning loss