Gated Knowledge Graph Neural Networks for Top-N Recommendation System

Abstract

In recent years, the knowledge graph based recommendation system is a research hotspot and scholars propose a propagation-based method, which combines graph neural networks with knowledge graph. But the previous work faces two problems: The first is that exist propagation methods generate neighours of target entity through random sampling strategy which will bring noise to the system. The second problem is that exist models directly aggregate the neighbors information of the target entity at each step, while ignoring the fact that the propagation of high-order information also needs to be selective and memorable. To solve these problems, we propose a novel model: Gated Knowledge Graph Neural Networks for Top-N Recommendation System(GKGNN). This model uses pretrain technique to generate neighbor set with high priority of the target entity in the graph. At the same time, this model introduces the gated mechanism into the propagation process and the valuable information is remembered and unimportant information is forgotten during the high-order information propagation. Finally, we conduct comparative experiments of our model with other baseline algorithms on three real world datasets, and the experimental results prove the superiority of our model.