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

Social relations are often used to improve recommendation quality when user-item
interaction data is sparse in recommender systems. Most existing social recommendation …

With the prevalence of social media, there has recently been a proliferation of
recommenders that shift their focus from individual modeling to group recommendation …

Graph Neural Networks (GNNs) have recently enabled substantial advances in graph
learning. Despite their rich representational capacity, GNNs remain under-explored for large …

Group recommendation aims to recommend items to a group of users. In this work, we study
group recommendation in a particular scenario, namely occasional group recommendation …

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

Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …

Self-supervised learning (SSL), which can automatically generate ground-truth samples
from raw data, holds vast potential to improve recommender systems. Most existing SSL …

As a step beyond traditional personalized recommendation, group recommendation is the
task of suggesting items that can satisfy a group of users. In group recommendation, the core …

Nowadays, it is more and more convenient for people to participate in group activities.
Therefore, providing some recommendations to groups of individuals is indispensable …