Recently, hyperbolic embedding has successfully demonstrated its superiority over
Euclidean analogues in representing hierarchical data. As the scale-free network that …

Surveillance systems employ object tracking methods, a class of automatic data analysis
algorithms, to accurately locate the target or its trajectory under a wide range of constraints …

Learning representations of both user interests and item characteristics is essentially
important for recommendation tasks. Although graph neural network-based methods have …

Insurance companies have focused on medicare fraud detection to reduce financial losses
and reputational harm because medicare fraud causes tens of billions of dollars in damage …

Many knowledge graphs, especially those that are collaboratively or automatically
generated, are prone to noise and cross-domain entries, which can impede domain-specific …

Groups where like-minded people gather to share interests, comments, or participate in
activities have recently gained popularity in well-known social platforms, such as Meetup …

In recommendation systems, the cold-start problem refers to the situation that users or items
have too few interaction events with the system to be recommended, such as clicks or …

User–item interactions on e-commerce platforms involve various intents, such as browsing
and purchasing, which require fine-grained intent recognition. Existing recommendation …

Abstract Knowledge graph representation learning entails transforming entities and
relationships within a knowledge graph into vectors to enhance downstream tasks. The rise …

Learning-to-rank from pairwise comparisons has a wide spectrum of applications in diverse
domains. For personalized recommendation applications, the widely used implicit feedback …