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 …

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

The federated recommendation system is an emerging AI service architecture that provides
recommendation services in a privacy-preserving manner. Using user-relation graphs to …

Recommender systems (RSs) have been extensively studied in academia and industry,
while few works focus on flash sale recommendations. In flash sale scenarios, period …

In many online recommendation services (eg, multimedia streaming, e-commerce),
predicting user's next behavior based on anonymous sessions remains a challenging …

Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior
sequence to predict the next item without the detailed user profile. Most recent works try to …

Session-based recommendation aims to predict the next clicked item based on the short-
term behavior sequence of an anonymous user, which is a challenging task owing to data …

One of the key challenges in sequential recommendation is how to extract and represent
user preferences. Traditional methods rely solely on predicting the next item. But user …

Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate
items because of their promising ability to extract features from user–item interactions and …

Session-based recommendation is a challenging task which predicts the next click based on
the short-term behavior of anonymous users. Compared to other recommendation models …