With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

As a key application of artificial intelligence, recommender systems are among the most
pervasive computer aided systems to help users find potential items of interests. Recently …

Neural graph based Collaborative Filtering (CF) models learn user and item embeddings
based on the user-item bipartite graph structure, and have achieved state-of-the-art …

Obtaining a well-trained model involves expensive data collection and training procedures,
therefore the model is a valuable intellectual property. Recent studies revealed that …

By treating users' interactions as a user-item graph, graph learning models have been
widely deployed in Collaborative Filtering~(CF) based recommendation. Recently …

With the emergence of social networks, social recommendation has become an essential
technique for personalized services. Recently, graph-based social recommendations have …

Recent advancements in recommender systems have focused on integrating knowledge
graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced …

While effective in recommendation tasks, collaborative filtering (CF) techniques face the
challenge of data sparsity. Researchers have begun leveraging contrastive learning to …

Social recommendation, which leverages social connections to construct Recommender
Systems (RS), plays an important role in alleviating information overload. Recently, Graph …