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 …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Traditional music recommender systems are mainly based on users' interactions, which limit
their performance. Particularly, various kinds of content information, such as metadata and …

Background: In health, it is important to promote the effectiveness, efficiency and adequacy
of the services provided; these concepts become even more important in the era of the …

Graph-structured data, where nodes exhibit either pair-wise or high-order relations, are
ubiquitous and essential in graph learning. Despite the great achievement made by existing …

Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to
perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial …

Most of the current hypergraph learning methodologies and benchmarking datasets in the
hypergraph realm are obtained by lifting procedures from their graph analogs …

Abstract Recently, Graph-based Few-Shot Learning (FSL) methods exhibit good
generalization by mining relations among few samples with Graph Neural Networks …

In this work, we introduce a hypergraph representation learning framework called
Hypergraph Neural Networks (HNN) that jointly learns hyperedge embeddings along with a …

Collaborative filtering (CF) algorithms provide personalized recommendations based on
user preferences and they are widely applied in various domains including social media and …