Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …

Graph neural network (GNN) is a powerful learning approach for graph-based recommender
systems. Recently, GNNs integrated with contrastive learning have shown superior …

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

Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …

Contrastive learning (CL) has recently been demonstrated critical in improving
recommendation performance. The underlying principle of CL-based recommendation …

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of
recommendation, since its ability to extract self-supervised signals from the raw data is well …

With the recent success of graph convolutional networks (GCNs), they have been widely
applied for recommendation, and achieved impressive performance gains. The core of …

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 …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …