Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

Many real-world relations can be represented by signed networks with positive and negative
links, as a result of which signed network analysis has attracted increasing attention from …

The remarkable achievements of Large Language Models (LLMs) have led to the
emergence of a novel recommendation paradigm—Recommendation via LLM (RecLLM) …

With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node
information and topological structure, have been demonstrated to be powerful in learning on …

Precise user and item embedding learning is the key to building a successful recommender
system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item …

Abstract Graph Neural Networks have been applied in recommender systems to learn the
representation of users and items from a user-item graph. In the state-of-the-art, there are …

Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …