Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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 …

Knowledge Graphs (KGs) have been utilized as useful side information to improve
recommendation quality. In those recommender systems, knowledge graph information …

Graph Neural Networks (GNNs) have become powerful tools in modeling graph-structured
data in recommender systems. However, real-life recommendation scenarios usually involve …

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 …

Recently, graph neural network (GNN) has become the new state-of-the-art approach in
many recommendation problems, with its strong ability to handle structured data and to …

A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …

The online emergence of multi-modal sharing platforms (eg, TikTok, Youtube) is powering
personalized recommender systems to incorporate various modalities (eg, visual, textual …

Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …

Accurate user and item embedding learning is crucial for modern recommender systems.
However, most existing recommendation techniques have thus far focused on modeling …