Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Incorporating knowledge graphs (KGs) into recommender systems to provide explainable
recommendation has attracted much attention recently. The multi-hop paths in KGs can …

The ability to explain why the model produced results in such a way is an important problem,
especially in the medical domain. Model explainability is important for building trust by …

Recommendation systems aim to provide users with relevant suggestions, but often lack
interpretability and fail to capture higher-level semantic relationships between user …

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG)
for recommendation. Existing GNN-based methods explicitly model the dependency …

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …

Recently, explainable recommendation has attracted increasing attentions, which can make
the recommender system more transparent and improve user satisfactions by …

Recommender systems have been demonstrated to be effective to meet user's personalized
interests for many online services (eg, E-commerce and online advertising platforms) …

Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and
higher-order interactions for recommender systems. However, compared with traditional …