In the current research, Graph Neural Networks (GNNs) play a decisive role in learning from
network data structure. In a social recommender system, GNNs have a significant …

Due to the remarkable ability to model the high-order links within user-item relations, the
graph neural network (GNN) is gradually applied to personalized recommendations in many …

Graph convolution neural networks have shown powerful ability in recommendation, thanks
to extracting the user-item collaboration signal from users' historical interaction information …

Hypercomplex algebras are well-developed in the area of mathematics. Recently, several
hypercomplex recommendation approaches have been proposed and yielded great …

Recommender system as an effective method to reduce information overload has been
widely used in the e-commerce field. Existing studies mainly capture semantic features by …

Traffic prediction for scenic spots is an important topic in modeling an urban traffic system.
Existing traffic prediction approaches typically use raw traffic data and road networks without …

Due to the success of graph convolutional networks (GCNs) in effectively extracting features
in non-Euclidean spaces, GCNs has become the rising star in implicit collaborative filtering …

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and
draw inferences from large scale graph-structured data in various application settings such …

推荐系统是实现海量互联网权益产品智能化推荐的重要手段. 为了提升个性化推荐的准确率,
提出了基于图计算方法的深度学习推荐系统. 针对用户行为数据存在多源异质的特性 …

目的潜在偏好, 优点是不需要对项目进行复杂的特征提取[6-7]. 协同过滤仅需要利用用户的历史
评分数据, 因此简单有效. 但是, 也存在数据的稀疏问题和冷启动问题[7]. 且推荐算法对捕捉用户 …