融合知识图谱和轻量级图卷积网络推荐系统的研究

基于协同过滤的算法是推荐系统中最重要的方法，由于冷启动和数据稀疏性的特点，限制了其推荐性能。为了应对以上问题，提出了知识图谱和轻量级图卷积网络推荐系统相结合的模型，该模型通过将知识图谱中的各个实体（项目）进行多次迭代嵌入传播以获取更多的高阶邻域信息，通过轻量聚合器进行聚合，进而预测用户和项目之间的评分。最后，在3个真实的数据集上MovieLens-20M、Last.FM和Book-Crossing的实验结果表明，该模型与其他基准模型相比可以得到较好的性能。

Research on the fusion of knowledge graph and lightweight graph convolutional network recommendation system

The algorithm based on collaborative filtering is the most important method in the recommendation system. However, the cold start and data sparsity characteristics limit its recommendation performance. We propose a model that combines a knowledge graph and a lightweight graph convolutional network recommendation system to address the aforementioned issues. The model embeds and propagates multiple items in the knowledge graph to obtain more high-order neighborhood information. It aggregates through a lightweight aggregator to predict the score between users and items. Finally, the experimental findings of MovieLens-20M, Last.FM and Book-Crossing on three real datasets show that compared with other benchmark models, this model can achieve better performance.