With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

The problem of data sparsity has long been a challenge in recommendation systems, and
previous studies have attempted to address this issue by incorporating side information …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

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 …

The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …

With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component, providing personalized …