Graph Neural Networks (GNNs) have emerged as powerful tools for collaborative filtering. A
key challenge of recommendations is to distill long-range collaborative signals from user …

Graph-based recommender system has attracted widespread attention and produced a
series of research results. Because of the powerful high-order connection modeling …

Collaborative filtering is one of the most fundamental topics for recommender systems.
Various methods have been proposed for collaborative filtering, ranging from matrix …

Recently, micro-video sharing platforms such as Kuaishou and Tiktok have become a major
source of information for people's lives. Thanks to the large traffic volume, short video …

Hypergraph Convolutional Network (HCN) has be-come a proper choice for capturing high-
order relationships. Existing HCN methods are tailored for static hypergraphs, which are …

Nowadays, most online services are hosted on multi-stakeholder marketplaces, where
consumers and producers may have different objectives. Conventional recommendation …

Graph neural networks (GNNs) aim to learn a low-dimensional feature for each vertex in the
graph from its input high-dimensional feature, by aggregating the features of the vertex's …

Session-based recommendation leverages anonymous sessions to predict which item a
user is most likely to click on next. While previous approaches capture items-transition …

Embedding learning of users and items can reveal latent interaction information in
recommender systems. Most existing recommendation approaches implicitly treat users and …

The graph neural network-based collaborative filtering (CF) models user-item interactions as
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …