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 …

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

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

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

Learning dynamic user preference has become an increasingly important component for
many online platforms (eg, video-sharing sites, e-commerce systems) to make sequential …

Modeling user preference from his historical sequences is one of the core problems of
sequential recommendation. Existing methods in this field are widely distributed from …

Next-location prediction is a special task of the next POIs recommendation. Different from
general recommendation tasks, next-location prediction is highly context-dependent:(1) …

Sequential recommendation aims at predicting users' preferences based on their historical
behaviors. However, this recommendation strategy may not perform well in practice due to …

Modeling user's long-term and short-term interests is crucial for accurate recommendation.
However, since there is no manually annotated label for user interests, existing approaches …