In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

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 …

Most modern recommender systems predict users' preferences with two components: user
and item embedding learning, followed by the user-item interaction modeling. By utilizing …

By treating users' interactions as a user-item graph, graph learning models have been
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …

Graph collaborative filtering (GCF) is a popular technique for capturing high-order
collaborative signals in recommendation systems. However, GCF's bipartite adjacency …

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

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

As recommender systems shift from rating-based to interaction-based models, graph neural
network-based collaborative filtering models are gaining popularity due to their powerful …

The ubiquity of implicit feedback makes them the default choice to build modern
recommender systems. Generally speaking, observed interactions are considered as …