In order to model the evolution of user preference, we should learn user/item embeddings
based on time-ordered item purchasing sequences, which is defined as Sequential …

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …

Modeling user preference from his historical sequences is one of the core problems of
sequential recommendation. Existing methods in this field are widely distributed from …

Sequential Recommendation characterizes the evolving patterns by modeling item
sequences chronologically. The essential target of it is to capture the item transition …

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

Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich
information they contain. By learning from EHRs, machine learning models can be built to …

Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

Collaborative filtering (CF) is widely used to learn informative latent representations of users
and items from observed interactions. Existing CF-based methods commonly adopt negative …

This paper explores meta-learning in sequential recommendation to alleviate the item cold-
start problem. Sequential recommendation aims to capture user's dynamic preferences …

Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …