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 …

The need for interpretable and accountable intelligent systems grows along with the
prevalence of artificial intelligence (AI) applications used in everyday life. Explainable AI …

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

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 …

Social recommendation aims to fuse social links with user-item interactions to alleviate the
cold-start problem for rating prediction. Recent developments of Graph Neural Networks …

With tremendous amount of recommendation algorithms proposed every year, one critical
issue has attracted a considerable amount of attention: there are no effective benchmarks for …

The growth of social media over the last decade has revolutionized the way individuals
interact and industries conduct business. Individuals produce data at an unprecedented rate …

Social recommendations utilize social relations to enhance the representation learning for
recommendations. Most social recommendation models unify user representations for the …

Recommender systems play an important role in helping online users find relevant
information by suggesting information of potential interest to them. Due to the potential value …

Location-based social networks (LBSNs) have attracted an inordinate number of users and
greatly enriched the urban experience in recent years. The availability of spatial, temporal …