As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath
knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of …

Knowledge graph (KG) contains well-structured external information and has shown to be
effective for high-quality recommendation. However, existing KG enhanced recommendation …

Knowledge graph (KG) plays an increasingly important role to improve the recommendation
performance and interpretability. A recent technical trend is to design end-to-end models …

Aiming to alleviate data sparsity and cold-start problems of tradi-tional recommender
systems, incorporating knowledge graphs (KGs) to supplement auxiliary information has …

Abstract Knowledge graphs (KGs) provide rich external structures and semantic information
for recommendation systems and have attracted extensive attention recently. Some existing …

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

Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

Abstract Knowledge graphs (KGs) have proven to be effective for high-quality
recommendation, where the connectivities between users and items provide rich and …

Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been
an influx of KG-enhanced recommendation methods. Most of existing methods are entirely …

It is commonly agreed that a recommender system should use not only explicit information
(ie, historical user-item interactions) but also implicit information (ie, incidental information) …