基于混合采样的图对比学习推荐算法

在推荐系统领域中，图卷积网络具有对于图结构数据更强的信息抽取能力。然而，现有的图卷积网络推荐算法主要关注改进模型结构，忽视了提高原始样本采样质量与挖掘用户—项目间隐式关系的重要性。针对上述问题，提出一种基于混合采样的图对比学习推荐算法。首先使用混合采样方法，提取出正样本中部分信息并将其注入负样本，从而生成全新的富含信息的难负样本；其次，通过轻量图卷积网络对难负样本进行特征提取，得到用户和项目的节点表征，采用邻域对比学习方法挖掘样本隐式关系；最后，利用多任务策略对推荐监督任务和对比学习任务进行联合优化。在真实数据集Yelp2018和Amazon-book上进行实验，采用recall和NDCG指标进行评估，实验结果表明，提出的模型相较其他基准模型取得了更好的效果。

Graph contrastive learning recommendation algorithm based on mixed sampling

In recommender systems, graph convolutional networks have stronger information extraction capabilities for graph-structured data. However, existing graph convolutional networks mainly focus on enhancing the model structure, ignoring the importance of improving the sampling quality of the original samples and mining the implicit relationship between users and items. Aiming at the above problems, this paper proposed a graph contrastive learning recommendation algorithm based on mixed sampling. Firstly, the algorithm used a mixed sampling method to extract part of the information in positive samples and injected them into negative samples, thereby generating new informative hard negative samples. Secondly, to extract features on hard negative samples, it used the light graph convolution network to obtain node representations of users and items. Finally, it carried out a multi-task strategy to jointly optimize the recommendation supervision task and the contrastive learning task. The experiments on real datasets Yelp2018 and Amazon-book demonstrate that the proposed algorithm improves performance compared with other recommendation algorithms in recall and NDCG evaluation indexes.