Side information fusion for sequential recommendation (SR) aims to effectively leverage
various side information to enhance the performance of next-item prediction. Most state-of …

The self-attention mechanism, which equips with a strong capability of modeling long-range
dependencies, is one of the extensively used techniques in the sequential recommendation …

Session-based recommendation (SR) gains increasing popularity because it helps greatly
maintain users' privacy. Aside from its efficacy, explainability is also critical for developing a …

Hypergraphs naturally represent group interactions, which are omnipresent in many
domains: collaborations of researchers, co-purchases of items, and joint interactions of …

Recently, recommender systems based on knowledge graphs (KGs) have become a
popular research direction. Graph neural network (GNN) is the key technology of KG-based …

Sequential recommendation (SR) aims to model user preferences by capturing behavior
patterns from their item historical interaction data. Most existing methods model user …

For sequential recommender, the coarse-grained yet sparse sequential signals mined from
massive user-item interactions have become the bottleneck to further improve the …

Graph contrastive learning (GCL) has been subject to more attention and been widely
applied to numerous graph learning tasks such as node classification and link prediction …

While motif has been widely employed in graph analytics, a fundamental question remains
open: How should overlapping motif edges connect into a path? Existing works address this …

Different from most conventional recommendation problems, sequential recommendation
focuses on learning users' preferences by exploiting the internal order and dependency …