Recommender systems exploit interaction history to estimate user preference, having been
heavily used in a wide range of industry applications. However, static recommendation …

Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …

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 …

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

The progress of recommender systems is hampered mainly by evaluation as it requires real-
time interactions between humans and systems, which is too laborious and expensive. This …

Abstract Graph Neural Networks have been applied in recommender systems to learn the
representation of users and items from a user-item graph. In the state-of-the-art, there are …

Next basket recommendation aims to infer a set of items that a user will purchase at the next
visit by considering a sequence of baskets he/she has purchased previously. This task has …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

Imbalanced classification on graphs is ubiquitous yet challenging in many real-world
applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) …