Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

In large-scale recommender systems, the user-item networks are generally scale-free or
expand exponentially. For the representation of the user and item, the latent features (aka …

The non-Euclidean geometry of hyperbolic spaces has recently garnered considerable
attention in the realm of representation learning. Current endeavors in hyperbolic …

Extensive user check-in data incorporating user preferences for location is collected through
Internet of Things (IoT) devices, including cell phones and other sensing devices in location …

Considering the prevalence of the power-law distribution in user-item networks, hyperbolic
space has attracted considerable attention and achieved impressive performance in the …

The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

Cognitive diagnosis (CD) aims to reveal the proficiency of students on specific knowledge
concepts and traits of test exercises (eg, difficulty). It plays a critical role in intelligent …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …