The recommendation system is an important and widely used technology in the era of Big
Data. Current methods have fused side information into it to alleviate the sparsity problem …

Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …

Collaborative filtering (CF) usually suffers from data sparsity and cold-start problems in real
recommendation scenarios, therefore, side information like social networks and contexts …

Collaborative filtering suffers from the issues of data sparsity and cold start. Due to which
recommendation models that only rely on the user–item interaction graph are insufficient to …

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings
based on the user-item bipartite graph structure, and have achieved state-of-the-art …

Collaborative filtering (CF) is an important research direction in recommender systems that
aims to make recommendations given the information on user-item interactions. Graph CF …

Abstract Graph Convolutional Networks~(GCNs) are state-of-the-art graph based
representation learning models by iteratively stacking multiple layers of convolution …

Dynamic recommendation is essential for modern recommender systems to provide real-
time predictions based on sequential data. In real-world scenarios, the popularity of items …

Personalized recommendation is ubiquitous, playing an important role in many online
services. Substantial research has been dedicated to learning vector representations of …

Traditional studies on recommender systems usually leverage only one type of user
behaviors (the optimization target, such as purchase), despite the fact that users also …