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

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

Recent studies show that graph neural networks (GNNs) are prevalent to model high-order
relationships for collaborative filtering (CF). Towards this research line, graph contrastive …

Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …

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 …

With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …

Graph Neural Networks (GNNs) generalize conventional neural networks to graph-
structured data and have received considerable attention owing to their impressive …

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

This paper presents a novel approach to representation learning in recommender systems
by integrating generative self-supervised learning with graph transformer architecture. We …

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