基于知识感知和跨层次对比学习的推荐方法  

Recommendation method based on knowledge‑awareness and cross-level contrastive learning

作　　者：郭洁 林佳瑜[2] 梁祖红 罗孝波 孙海涛 GUO Jie;LIN Jiayu;LIANG Zuhong;LUO Xiaobo;SUN Haitao(School of Computer Science and Technology,Guangdong University of Technology,Guangzhou Guangdong 510006,China;Library,Guangdong University of Technology,Guangzhou Guangdong 510006,China;Experimental Teaching Department,Guangdong University of Technology,Guangzhou Guangdong 510006,China)

机构地区：[1]广东工业大学计算机学院,广州510006 [2]广东工业大学图书馆,广州510006 [3]广东工业大学实验教学部,广州510006

出　　处：《计算机应用》2024年第4期1121-1127,共7页journal of Computer Applications

基　　金：教育部产学合作协同育人项目(220901229305933)。

摘　　要：知识图谱(KG)作为一种辅助信息能够有效提高推荐模型的推荐质量,但现有的基于图神经网络(GNN)的知识感知推荐模型存在节点信息利用不均衡问题。为此,提出一种基于知识感知和跨层次对比学习的推荐方法(KCCL)。所提方法在GNN的知识感知推荐模型基础上引入对比学习范式,以缓解稀疏的交互数据和嘈杂的KG在信息聚合时节点间依赖的关系偏离真实表示导致节点信息利用不均衡的问题。首先,将用户–物品交互图和物品知识图整合为一个异质图,并通过基于图注意力机制的GNN实现用户和物品的节点表示;其次,在信息传播聚合层中加入一致的噪声进行数据增强,得到不同阶层的节点表示,并将获得的最外层节点表示与最内层节点表示进行跨层次对比学习;最后,联合优化推荐监督任务和对比学习辅助任务,得到最终各节点表示。在DBbook2014和MovieLens-1m数据集上的实验结果显示,相较于次优对比方法,KCCL的Recall@10分别提升了3.66%和0.66%,NDCG@10分别提升了3.57%和3.29%,验证了KCCL的有效性。As a kind of side information,Knowledge Graph(KG)can effectively improve the recommendation quality of recommendation models,but the existing knowledge-awareness recommendation methods based on Graph Neural Network(GNN)suffer from unbalanced utilization of node information.To address the above problem,a new recommendation method based on Knowledge‑awareness and Cross-level Contrastive Learning(KCCL)was proposed.To alleviate the problem of unbalanced node information utilization caused by the sparse interaction data and noisy knowledge graph that deviate from the true representation of inter-node dependencies during information aggregation,a contrastive learning paradigm was introduced into knowledge-awareness recommendation model of GNN.Firstly,the user-item interaction graph and the item knowledge graph were integrated into a heterogeneous graph,and the node representations of users and items were realized by a GNN based on the graph attention mechanism.Secondly,consistent noise was added to the information propagation aggregation layer for data augmentation to obtain node representations of different levels,and the obtained outermost node representation was compared with the innermost node representation for cross-level contrastive learning.Finally,the supervised recommendation task and the contrastive learning assistance task were jointly optimized to obtain the final representation of each node.Experimental results on DBbook2014 and MovieLens-1m datasets show that compared to the second prior contrastive method,the Recall@10 of KCCL is improved by 3.66%and 0.66%,respectively,and the NDCG@10 is improved by 3.57%and 3.29%,respectively,which verifies the effectiveness of KCCL.

关 键 词：知识图谱 图神经网络 知识感知 图注意力机制 对比学习 数据增强 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]