基于知识图谱和麻雀算法改进的图卷积网络推荐模型

Improved graph convolution network recommendation model based on knowledge graph and Sparrow Search Algorithm

现有的知识图谱推荐模型通过聚合实体的高阶领域信息学习用户的远程潜在兴趣.这些方法存在两个问题:①通过计算用户和关系之间的分数得到固定大小的实体邻域结构,不能充分利用知识图谱中的全局信息;②现有模型以相同的权值对实体的邻居节点进行聚合,没有考虑到目标实体对不同采样邻居的偏好程度不同.基于上述问题,提出了融合邻居节点重要度采样和特征交叉池化的图卷积推荐模型.首先,通过融合邻居节点的分数和其中心性感知分数得到邻居节点重要度;然后,引入特征交叉池化层对目标实体向量和邻域向量进行特征交叉后聚合,得到最终的实体特征表示;最后,使用改进的麻雀算法优化图卷积神经网络的超参数.在3个数据集上对模型的推荐性能进行验证,相比于基线模型,在AUC和F1指标上平均提升了3.0%和2.4%.

The existing knowledge graph recommendation models learn the user's remote potential interest by aggregating the high-order domain information of the entity.There are two problems in these methods.Firstly,the fixed size of the entity neighborhood structure is obtained by calculating the score between the user and the relationship,which cannot make full use of the global information in the knowledge graph.Secondly,the existing models aggregate the neighbor nodes of the entity with the same weight,without considering the preference of the target entity for different sampling neighbors.Based on the above problems,this paper proposes a graph convolution recommendation model which combine neighbor importance sampling and feature cross pooling.The sampling method based on neighbor importance obtains the importance of neighbor nodes by fusing the scores of neighbor nodes and the centrality perception scores,and then introduces the Bi-Interaction pooling layer to carry out the feature crossover and aggregation of the target entity vector and the neighborhood vector to obtain the final entity feature representation.Finally,the improved Sparrow Search Algorithm(SSA)is used to optimize the hyperparameters of Graph Convolutional Neural(GCN)network.This paper verifies the performance of the improved model on three data sets.Compared with baseline models,AUC and F1 indexes are increased by 3.0%and 2.4%.