In recommendation, graph-based Collaborative Filtering (CF) methods mitigate the data sparsity by introducing Graph Contrastive Learning (GCL). However, the random negative sampling strategy in these GCL-based CF models neglects the semantic structure of users (items), which not only introduces false negatives (negatives that are similar to anchor user (item)) but also ignores the potential positive samples. To tackle the above issues, we propose Topology-aware Debiased Self-supervised Graph Learning (TDSGL) for recommendation, which constructs contrastive pairs according to the semantic similarity between users (items). Specifically, since the original user-item interaction data commendably reflects the purchasing intent of users and certain characteristics of items, we calculate the semantic similarity between users (items) on interaction data. Then, given a user (item), we construct its negative pairs by selecting users (items) which embed different semantic structures to ensure the semantic difference between the given user (item) and its negatives. Moreover, for a user (item), we design a feature extraction module that converts other semantically similar users (items) into an auxiliary positive sample to acquire a more informative representation. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on three public datasets. Our model implementation codes are available at https://github.com/malajikuai/TDSGL.

翻译：在推荐系统中，基于图的协同过滤方法通过引入图对比学习来缓解数据稀疏性问题。然而，这些基于GCL的CF模型中的随机负采样策略忽略了用户（物品）的语义结构，这不仅引入了错误负例（与锚点用户（物品）相似的反例），还忽视了潜在的正例样本。为解决上述问题，我们提出了一种面向推荐的无偏拓扑感知自监督图学习方法TDSGL，该方法根据用户（物品）间的语义相似性构建对比对。具体而言，由于原始用户-物品交互数据能良好反映用户的购买意图及物品的特定属性，我们基于交互数据计算用户（物品）间的语义相似度。随后，针对给定用户（物品），通过选取语义结构嵌入不同的用户（物品）构建负例对，确保目标用户（物品）与其负例间的语义差异性。此外，我们设计了一个特征提取模块，将其他语义相似的用户（物品）转换为辅助正样本，从而获取更具信息量的表示。实验结果表明，在三个公开数据集上，所提模型显著优于当前最先进模型。模型实现代码已开源至https://github.com/malajikuai/TDSGL。