To provide more accurate, diverse, and explainable recommendation, it is compulsory to go
beyond modeling user-item interactions and take side information into account. Traditional …

Knowledge graph (KG) plays an increasingly important role in recommender systems. A
recent technical trend is to develop end-to-end models founded on graph neural networks …

Knowledge graph is generally incorporated into recommender systems to improve overall
performance. Due to the generalization and scale of the knowledge graph, most knowledge …

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

Since it can effectively address the problem of sparsity and cold start of collaborative
filtering, knowledge graph (KG) is widely studied and employed as side information in the …

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 …

Knowledge graphs (KGs) have proven to be effective to improve recommendation. Existing
methods mainly rely on hand-engineered features from KGs (eg, meta paths), which …

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

To alleviate sparsity and cold start problem of collaborative filtering based recommender
systems, researchers and engineers usually collect attributes of users and items, and design …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …