Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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 …

Current sequential recommender systems are proposed to tackle the dynamic user
preference learning with various neural techniques, such as Transformer and Graph Neural …

Sequential recommendation aims to capture users' dynamic preferences, in which data
sparsity is a key problem. Most contrastive learning models leverage data augmentation to …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …

This paper studies the problem of traffic flow forecasting, which aims to predict future traffic
conditions on the basis of road networks and traffic conditions in the past. The problem is …

Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next
position a user would visit, thus providing appealing location advice. In light of this, graph …

Sequential recommendation effectively addresses information overload by modeling users'
temporal and sequential interaction patterns. To overcome the limitations of supervision …

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

Sequential recommendation aims at understanding user preference by capturing successive
behavior correlations, which are usually represented as the item purchasing sequences …