Recommender systems are essential tools in the digital era, providing personalized content
to users in areas like e-commerce, entertainment, and social media. Among the many …

The Bayesian personalized ranking (BPR) loss is a commonly used objective in training
recommender systems, upon which various auxiliary graph-based self-supervised …

Graph neural networks have become the standard learning architectures in graph-based
learning and achieve great progress in real-world tasks. Existing graph neural network …

Recommending suitable services to users autonomously has become the key to solve the
problem of service information overload. Existing recommendation algorithms have some …

Precise representation learning to an undirected weighted network (UWN) is the foundation
of understanding its connection patterns and functional mechanisms. A graph convolution …

Graph convolutional networks (GCNs) have substantially advanced state-of-the-art
collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore …

The relationship between users and items, which cannot be recovered by traditional
techniques, can be extracted by the recommendation algorithm based on the graph …

Bipartite graphs are rich data structures with prevalent applications and characteristic
structural features. However, less is known about their growth patterns, particularly in …

Many recent recommendation systems leverage the large quantity of reviews placed by
users on items. However, it is both challenging and important to accurately measure the …

The attributes of users and items contain key information for recommendation. The latest
advances demonstrate that better representations can be learned by performing graph …