In recent years, deep learning (DL) has been the most popular computational approach in
the field of machine learning (ML), achieving exceptional results on a variety of complex …

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

Recommendation systems have witnessed significant advancements and have been widely
used over the past decades. However, most traditional recommendation methods are task …

The debut of ChatGPT has recently attracted significant attention from the natural language
processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT …

Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people's daily lives. Since recommendations involve …

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing
users and items into latent representation space, with their correlative patterns from …

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

Recently, graph collaborative filtering methods have been proposed as an effective
recommendation approach, which can capture users' preference over items by modeling the …

This paper reviews the research trends that link the advanced technical aspects of
recommendation systems that are used in various service areas and the business aspects of …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …