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 …

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 …

With the recent success of graph convolutional networks (GCNs), they have been widely
applied for recommendation, and achieved impressive performance gains. The core of …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

COVID‐19 pneumonia started in December 2019 and caused large casualties and huge
economic losses. In this study, we intended to develop a computer‐aided diagnosis system …

Collaborative filtering (CF) is a widely studied research topic in recommender systems. The
learning of a CF model generally depends on three major components, namely interaction …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

Hyperbolic spaces offer a rich setup to learn embeddings with superior properties that have
been leveraged in areas such as computer vision, natural language processing and …

With the increasing amount of information on the internet, recommendation system (RS) has
been utilized in a variety of fields as an efficient tool to overcome information overload. In …

Multi-view clustering (MVC) enhances the clustering performance of data by combining
correlation information from different views. However, most existing MVC approaches …