In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, which is better than existing benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet.

翻译：本文提出了一种新颖的基于图神经网络的推荐模型KGLN，该模型利用知识图谱（KG）信息增强个性化推荐的准确性和有效性。首先，我们使用单层神经网络融合图中各节点的特征，然后通过引入影响因素调整相邻实体的聚合权重。该模型通过迭代从单层演变为多层，使实体能够获取广泛的多阶关联实体信息。最后一步整合实体与用户的特征以生成推荐评分。通过比较不同聚合方法和影响因素的效果，评估了模型性能。在MovieLen-1M和Book-Crossing数据集上的测试中，KGLN的ROC曲线下面积（AUC）分别提升了0.3%至5.9%和1.1%至8.2%，优于现有基准方法如LibFM、DeepFM、Wide&Deep和RippleNet。