Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

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

Recommender systems learn from historical user-item interactions to identify preferred items
for target users. These observed interactions are usually unbalanced following a long-tailed …

Self-supervised sequential recommendation significantly improves recommendation
performance by maximizing mutual information with well-designed data augmentations …

Sequential recommendation has now been more widely studied, characterized by its well-
consistency with real-world recommendation situations. Most existing works model user …

Cross-domain recommendation (CDR), which leverages information collected from other
domains, has been empirically demonstrated to effectively alleviate data sparsity and cold …

Deep learning classifiers achieve state-of-the-art performance in various risk detection
applications. They explore rich semantic representations and are supposed to automatically …

Continual graph learning (CGL) studies the problem of learning from an infinite stream of
graph data, consolidating historical knowledge, and generalizing it to the future task. At …

Content caching in mobile networks is a highly promising technology for reducing traffic load
latency and energy consumption levels. Its fundamental goal is to satisfy the supply-and …