融合协同知识图谱高阶邻居特征的推荐模型

在推荐时引入知识图谱中的实体及关系信息是有效缓解冷启动问题的方法. HAN模型首次将基于注意力机制的图神经网络用于异构图, 但是并没有充分利用节点的高阶邻居信息. 为了解决该问题, 提出了一种融合协同知识图谱高阶邻居特征的推荐模型CKG-HAN. 该模型用元路径来连接项目节点, 将协同知识图谱分成多个子图, 模型的节点注意力层用于聚合子图中每个节点的高阶邻居特征, 关系注意力层给不同元路径下的节点特征分配不同的权重, 最终得到充分融合语义信息的节点嵌入表示. 在MovieLens-1M数据集上进行了Top-K推荐, 结果表明本文提出的模型能够有效提高推荐结果的准确性.

Recommendation Model Fused with High-order Neighbor Features of Collaborative Knowledge Graph

Introducing the entity and relationship information in the knowledge graph during recommendation is an effective way to alleviate the problem of cold start. The HAN model introduces the attention mechanism-based graph neural networks into heterogeneous graphs for the first time. However, it does not make full use of the high-order neighbor information of nodes. To solve this problem, the study proposes a recommendation model CKG-HAN that integrates the high-order neighbor features of the collaborative knowledge graph. The model employs meta-paths to connect project nodes and divides the collaborative knowledge graph into multiple subgraphs. The high-order neighbor features of each node in the subgraph are aggregated in the node attention layer of the model, and different weights are assigned to node features on different meta-paths by the relation attention layer. Finally, a node embedding representation is obtained which fully integrates semantic information. The Top-K recommendation is performed on the MovieLens-1M data set, and the results show that the model proposed in this study can effectively improve the accuracy of the recommendation results.