In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Cross-domain recommendation (CDR) has become an important research direction in the
field of recommender systems due to the increasing demand for personalized …

Cross-domain recommendation (CDR) improves recommendation accuracy by transferring
knowledge from rich domains to sparse domains, which is a significant advancement in the …

Due to the strong representation capabilities of graph structures in social networks, social
relationships are often used to improve recommendation quality. Most existing social …

In recent years, researchers have attempted to exploit social relations to improve the
performance in recommendation systems. Generally, most existing social recommendation …

Fusion of multi-source information is one of the primary methods to alleviate data sparsity in
recommender systems (RS). Hypergraphs have shown remarkable capabilities in dealing …

When user–item interaction data are extremely sparse or even missing, establishing
accurate user interest models is a critical challenge that our work addresses by proposing a …

Recommender systems are an effective solution to address the issue of information overload
and a thriving research field. This paper focuses on the efficient mining of user-item …

Recommendation systems offer an effective solution to information overload, finding
widespread application across e-commerce, news platforms, and beyond. By analyzing …