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 …

Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

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

Much of the recent progress in sequential and session-based recommendation has been
driven by improvements in model architecture and pretraining techniques originating in the …

Session-based recommendation targets next-item prediction by exploiting user behaviors
within a short time period. Compared with other recommendation paradigms, session-based …

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

Modeling customer shopping intentions is a crucial task for e-commerce, as it directly
impacts user experience and engagement. Thus, accurately understanding customer …

Predicting a user's preference in a short anonymous interaction session instead of long-term
history is a challenging problem in the real-life session-based recommendation, eg, e …