结合超图对比学习和关系聚类的知识感知推荐算法

Knowledge-aware Recommendation Algorithm Combining Hypergraph Contrast Learning and Relational Clustering

结合知识图谱的推荐算法通过引入知识图谱,获得项目的辅助信息,从而实现更好的推荐效果。然而推荐的过程中存在知识图谱中关系长尾分布、用户项目交互数据稀疏和异构信息利用不平衡的问题。针对这些问题,提出了一种结合超图对比学习和关系聚类的知识感知推荐算法(HC-CRKG)。通过关系聚类的方式重构知识图谱,缓解了知识图谱中关系的长尾分布问题;构建用户-项目-实体异构图,利用一种结合注意力机制的图卷积网络学习用户、项目的异构图嵌入;同时使用一种参数化的超图卷积网络,学习用户、项目的超图嵌入;在异构图嵌入和超图嵌入之间进行对比学习,为模型引入自监督信号,缓解数据稀疏性问题;将异构图嵌入和超图嵌入相结合,用于后续的推荐预测,进一步缓解了异构信息利用不平衡问题。模型在MovieLens-1M、Book-Crossing和Last.FM三个公开数据集上与CKAN、KGIC、VRKG4Rec等基线模型进行对比实验,实验结果表明在AUC、F1和Recall@K指标上,模型均取得了不同程度的提升。

The recommendation algorithm combined with knowledge graph obtains the auxiliary information of items by introducing knowledge graph to achieve better recommendation effect.However,there are problems in the process of recommendation:long-tail distribution of relations in the knowledge graph,sparse user-item interaction data and unbalanced utilization of heterogeneous information.In response to these problems,a knowledge-aware recommendation algorithm combining hypergraph contrast learning and relational clustering(HC-CRKG)is proposed.Firstly,the knowledge graph is reconstructed by the way of relationship clustering,which alleviates the problem of long-tail distribution of relationships in the knowledge graph.Secondly,a user-item-entity heterogeneous graph is constructed,and a graph convolutional network combining attention mechanism is used to learn the heterogeneous graph embeddings of users and items.Meanwhile,a parametric hypergraph convolutional network is used to learn the hypergraph embeddings of users and items.Subsequently,contrast learning is performed between the heterogeneous graph embedding and the hypergraph embedding to introduce a self-supervised signal for the model to alleviate the data sparsity problem.Finally,the heterogeneous graph embedding and hypergraph embedding are combined for subsequent recommendation prediction,which further alleviates the heterogeneous information utilization imbalance problem.The model is tested against baseline models such as CKAN(collaborative knowledge-aware attentive network),KGIC(improving knowledge-aware recommendation with multi-level interactive contrastive learning),and VRKG4Rec(virtual relational knowledge graphs for recommendation)on three publicly available datasets MovieLens-1M,Book-Crossing and Last.FM.Experimental results show that the model achieves different degrees of improvement in AUC,F1 and Recall@K.