Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Recently, graph neural network (GNN) has become the new state-of-the-art approach in
many recommendation problems, with its strong ability to handle structured data and to …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …

Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …

With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …

Social recommendation aims to fuse social links with user-item interactions to alleviate the
cold-start problem for rating prediction. Recent developments of Graph Neural Networks …