As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Graph Convolutional Network (GCN) has achieved extraordinary success in learning
representations of nodes in graphs. However, regarding Heterogeneous Information …

Heterogeneous social networks, which are characterized by diverse interaction types, have
resulted in new challenges for missing link prediction. Most deep learning models tend to …

Pretrained Language Models (PLMs) such as BERT have revolutionized the landscape of
Natural Language Processing (NLP). Inspired by their proliferation, tremendous efforts have …

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

Modeling time-evolving preferences of users with their sequential item interactions, has
attracted increasing attention in many online applications. Hence, sequential recommender …

With the rapid development of online social recommendation system, substantial methods
have been proposed. Unlike traditional recommendation system, social 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 …