Traditional recommendation systems are faced with two long-standing obstacles, namely
data sparsity and cold-start problems, which promote the emergence and development of …

Recently, large language models (LLMs)(eg, GPT-4) have demonstrated impressive general-
purpose task-solving abilities, including the potential to approach recommendation tasks …

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

Data sparsity is a long-standing problem in recommender systems. To alleviate it, Cross-
Domain Recommendation (CDR) has attracted a surge of interests, which utilizes the rich …

Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

With the increasing abundance of information resources and the development of deep
learning techniques, recommender systems (RSs) based on deep learning have gradually …

A well-designed recommender system can accurately capture the attributes of users and
items, reflecting the unique preferences of individuals. Traditional recommendation …

Recommender systems have been widely deployed in many real-world applications, but
usually suffer from the long-standing user cold-start problem. As a promising way, Cross …

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to
improve the target behavior's performance. We argue that MBR models should:(1) model the …

Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …