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 …

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 …

Collaborative filtering often suffers from sparsity and cold start problems in real
recommendation scenarios, therefore, researchers and engineers usually use side …

Most popular recommender systems learn the embedding of users and items through
capturing valuable information from user–item interactions or item knowledge graph (KG) …

To address the sparsity and cold-start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve the …

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 …

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

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 …

To address the sparsity and cold start problem of collaborative filtering, researchers usually
make use of side information, such as social networks or item attributes, to improve …

Abstract Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold
start problems of collaborative filtering. Recently, graph neural networks (GNNs) have been …