Recommender systems have become an essential tool to help resolve the information
overload problem in recent decades. Traditional recommender systems, however, suffer …

Social and information networking activities such as on Facebook, Twitter, WeChat, and
Weibo have become an indispensable part of our everyday life, where we can easily access …

As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

Collaborative filtering (CF) has been widely employed within recommender systems to solve
many real-world problems. Learning effective latent factors plays the most important role in …

Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …

In general, recommendation can be viewed as a matching problem, ie, match proper items
for proper users. However, due to the huge semantic gap between users and items, it's …

The increasing popularity of real-world recommender systems produces data continuously
and rapidly, and it becomes more realistic to study recommender systems under streaming …

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

Social influence is essential to social recommendation. Current influence-based social
recommendation focuses on the explicit influence on observed social links. However, in real …

Existing neural collaborative filtering (NCF) recommendation methods suffer from severe
sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected …