Abstract 3D point clouds are a crucial type of data collected by LiDAR sensors and widely
used in transportation applications due to its concise descriptions and accurate localization …

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order
relationships for collaborative filtering (CF). Towards this research line, graph contrastive …

Recommender systems have seen significant advancements with the influence of deep
learning and graph neural networks, particularly in capturing complex user-item …

With the explosive growth of online information, the significant application value of
recommender systems has received considerable attention. Since user–item interactions …

This paper presents a novel approach to representation learning in recommender systems
by integrating generative self-supervised learning with graph transformer architecture. We …

While some powerful neural network architectures (eg, Transformer, Graph Neural
Networks) have achieved improved performance in sequential recommendation with high …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Recently, deep clustering utilizing Graph Neural Networks has shown good performance in
the graph clustering. However, the structure information of graph was underused in existing …

Graph neural networks (GNNs) have shown the power in representation learning over graph-
structured user-item interaction data for collaborative filtering (CF) task. However, with their …

Graph Neural Networks (GNNs) have garnered considerable interest due to their
exceptional performance in a wide range of graph machine learning tasks. Nevertheless, the …