结合自我特征和对比学习的推荐模型

Recommendation model combining self-features and contrastive learning

针对图神经网络推荐中图卷积在消息传递过程的嵌入表示过平滑和噪声问题,提出一种结合自我特征和对比学习的推荐模型(SfCLRec)。采用预训练-正式训练架构训练模型,首先预训练用户和项目的嵌入表示,通过融合节点自我特征维持节点本身的特征唯一性,并引入层级对比学习任务减少来自高阶邻居节点中的噪声;其次,在正式训练阶段根据评分机制重新构建协同图邻接矩阵;最后,根据最终嵌入得到预测评分。实验结果表明,相较于LightGCN、SimGCL(Simple Graph Contrastive Learning)等现有图神经网络推荐模型,SfCLRec在3个公开数据集ML-latest-small、Last.FM和Yelp中均取得了较好的召回率和归一化折损累计增益(NDCG),验证了SfCLRec的有效性。

Aiming at the over-smoothing and noise problems in the embedding representation in the message passing process of graph convolution based on graph neural network recommendation,a Recommendation model combining Selffeatures and Contrastive Learning(SfCLRec)was proposed.The model was trained using a pre-training-formal training architecture.Firstly,the embedding representations of users and items were pre-trained to maintain the feature uniqueness of the nodes themselves by fusing the node self-features and a hierarchical contrastive learning task was introduced to mitigate the noisy information from the higher-order neighboring nodes.Then,the collaborative graph adjacency matrix was reconstructed according to the scoring mechanism in the formal training stage.Finally,the predicted score was obtained based on the final embedding.Compared with existing graph neural network recommendation models such as LightGCN and Simple Graph Contrastive Learning(SimGCL),SfCLRec achieves the better recall and NDCG(Normalized Discounted Cumulative Gain)in three public datasets ML-latest-small,Last.FM and Yelp,validating the effectiveness of SfCLRec.