Contrastive Graph Semantic Learning via prototype for recommendation

Recommendation systems serve a vital function in modern information access and e-commerce. However, traditional collaborative filtering methods often face significant challenges such as data sparsity, sampling bias, and noise. To address these issues, we propose a novel model called Contrastive Graph Semantic Learning (CGSL). Our approach specifically tackles data sparsity by capturing the underlying semantic structure, thus improving the quality of the recommendation through improved user preference representation. To counteract sampling bias, CGSL employs a reweighted negative sampling algorithm that reduces the occurrence of misleading "false" negatives, which can skew user interest differentiation. Additionally, the prototype-based contrastive learning component of our model allows for better discrimination between user interactions and item features, addressing the noise present in traditional methods. We demonstrate the effectiveness of CGSL through the visualization of t-SNE in the Yelp2018 and Amazon-Book datasets, where it successfully clusters similar user behaviors and item attributes. Experimental results demonstrate that CGSL outperforms state-of-the-art methods by enhancing the precision of recommendations, minimizing biases, and preserving the intrinsic structure of datasets. These findings validate CGSL as a robust and effective solution for advancing recommendation system performance.