计算机科学与探索2024，Vol.18Issue(8)：2140-2155,16.DOI:10.3778/j.issn.1673-9418.2305058
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
摘要
Abstract
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 pro-posed.Firstly,the knowledge graph is reconstructed by the way of relationship clustering,which alleviates the prob-lem 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 hetero-geneous 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.关键词
推荐系统/知识图谱/图卷积网络/超图/对比学习/自监督学习/知识表示学习Key words
recommendation system/knowledge graph/graph convolution networks/hypergraph/constract learning/self-supervised learning/knowledge representation learning分类
信息技术与安全科学引用本文复制引用
王永贵,陈书铭,刘义海,赖贞祥..结合超图对比学习和关系聚类的知识感知推荐算法[J].计算机科学与探索,2024,18(8):2140-2155,16.基金项目
国家自然科学基金(61772249). This work was supported by the National Natural Science Foundation of China(61772249). （61772249）
