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 …

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

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …

Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …

Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …

Social recommendation has emerged to leverage social connections among users for
predicting users' unknown preferences, which could alleviate the data sparsity issue in …

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 …

Recommender systems provide essential web services by learning users' personal
preferences from collected data. However, in many cases, systems also need to forget some …

Recommendation systems play a vital role to keep users engaged with personalized
contents in modern online platforms. Recently, deep learning has revolutionized many …

Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …