Recommendation from implicit feedback is a highly challenging task due to the lack of
reliable negative feedback data. Only positive feedback are observed and the unobserved …

Recommender systems in social networks are widely used for connecting users to their
desired items from a vast catalog of available items. Learning the user's preferences from all …

The explicitly observed social relations from online social platforms have been widely
incorporated into recommender systems to mitigate the data sparsity issue. However, the …

Online communities such as Facebook and Twitter are enormously popular and have
become an essential part of the daily life of many of their users. Through these platforms …

In many applications of session-based recommendation, social networks are usually
available. Since users' interests are influenced by their friends, recommender systems can …

Recommender systems are used to accurately and actively provide users with potentially
interesting information or services. Collaborative filtering is a widely adopted approach to …

The pervasive presence of social media greatly enriches online users' social activities,
resulting in abundant social relations. Social relations provide an independent source for …

Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …

Data in many real-world applications such as social networks, users shopping behaviors,
and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) …

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem
results in the selection bias issue, degrading the recommendation performance ultimately. A …