融合元图邻域的知识图谱推荐模型

Knowledge graph recommendation model with integratedmeta-graph neighborhoods

基于知识图谱的主流推荐模型在融合高阶信息时较少考虑源节点与目标节点之间的关系,在复杂网络场景中易引入过多噪声信息进而影响推荐性能。针对此问题提出一种融合元图邻域的知识图谱推荐模型,通过构建并融合元图邻域降低噪声信息的影响,提升推荐性能。首先,基于元图相似度生成源节点的初始相似序列,利用自注意力网络与线性网络对初始序列进行特征增强,以增强后的特征向量组成的集合构造节点的元图邻域。其次,基于用户对各个元图的不同偏好程度设计注意力机制,对所得元图邻域进行卷积聚合,将元图邻域融入源节点,增强源节点的特征表示。最后,以增强后的向量与用户向量的内积作为用户与项目交互的概率,并以此完成推荐。在MovieLens-20M与Last-FM数据集上进行实验,AUC与F_(1)值分别为97.3%和83.1%、94.3%和75.6%,recall@50分别为35.4%与31.7%,其表现优于NGCF、KGCN、LKGR等模型。结果表明,融合元图邻域的知识图谱推荐模型可以有效提升推荐的性能。

Mainstream knowledge graph-based recommendation model rarely consider the relationship between source nodes and target nodes when fusing high-order information,leading to the introduction of too much noise information and thus affec-ting recommendation performance in complex network scenarios.To address this problem,this paper proposed a knowledge graph recommendation model with integrated meta-graph neighborhoods,with the goal of reducing the impact of noise information by constructing and integrating meta-graph neighborhoods,thereby improving recommendation performance.Firstly,the model obtained the initial similar sequence of the source node based on meta-graph similarity.Then,the model enhanced the initial sequence using self-attention networks and linear networks,which resulted in a set of enhanced feature vectors that serve as the meta-graph neighborhoods of the node.Secondly,the model designed an attention mechanism based on the user’s different preferences for each meta-graph to perform convolution and aggregation on the resulting meta-graph neighborhoods.Then,the model integrated the meta-graph neighborhoods into the source node to enhance the feature representation of the source node.Finally,the model used the inner product of the enhanced vector and the user vector as the probability of user interaction with the item,which was then utilized to complete the recommendation.Experimental results on the MovieLens-20M and Last-FM datasets show that the proposed model achieves an AUC of 97.3%and 94.3%,and F_(1)-score of 83.1%and 75.6%,respectively.The recall@50 are 35.4%and 31.7%,respectively.These performance metrics outperform models such as NGCF,KGCN,LKGR,and other models.The results demonstrate that the knowledge graph recommendation model with integrated meta-graph neighborhoods is effective in improving recommendation performance.