基于异质信息网络的推荐模型

为了解决推荐系统的冷启动和稀疏性问题, 本文提出了一种基于异质信息网络的推荐模型. 传统的推荐方法无法在知识图谱表示学习中融入隐含的路径信息, 这样使得知识推荐系统性能较为一般. 本文提出的模型在异质信息网络中设置元路径, 通过图神经网络融入到知识图谱表示学习中. 再利用注意力网络连接推荐任务和知识图谱表示任务, 其可以学习两个任务之中潜在的特征, 并且能够增强推荐系统中被推荐项和知识图谱中实体的相互作用. 最后在推荐任务中进行用户点击率预测. 模型在公开数据集Book-Crossing和通过DBLP数据集构建的图谱上进行了实验. 最后结果表明, 模型在AUC, 召回率和F1值3个指标上均比其他算法有更好的表现.

Recommendation Model Based on Heterogeneous Information Network

To address the cold-start and sparsity problems of recommendation systems, this study proposes a recommendation model based on a heterogeneous information network. Previous approaches are unable to take into account both knowledge graph representation learning and implicit path information, which makes the performance of knowledge recommendation systems mediocre. The proposed method sets meta-paths in the heterogeneous information network and integrates them into knowledge graph representation learning by the graph neural network (GNN). Next, the attention network is used to connect a recommendation task with a knowledge graph representation task. It can not only learn the potential features of the two tasks but also enhance the interactions between the recommended items in the recommendation system and the entities in the knowledge graph. Finally, the user click rate is predicted in the recommendation task. The method is experimented on the open dataset Book-Crossing and the knowledge graph constructed with the DBLP dataset, and the results demonstrate that the proposed model achieves better performance than that of other algorithms in indexes of area under curve (AUC), recall, and F1-score.