Collaborative filtering recommender systems (CFRSs) have already been proved effective to
cope with the information overload problem since they merged in the past two decades …

Abstract Knowledge graph (KG) can provide auxiliary information for recommender system
to alleviate the sparsity and cold start problems, while graph convolutional networks (GCN) …

With the development of big data and social computing, large-scale group decision making
(LGDM) problems attract much attention and become merging with social networks or …

Music recommender systems is an important field of research because of easy availability
and use of online music. The most existing models only focus on explicit data like ratings …

Clustering based recommender systems have been demonstrated to be efficient and
scalable to large-scale datasets. However, due to the employment of dimensionality …

Online social networks have provided an appropriate infrastructure for users to interact with
one another and share information. Since trust is one of the most important factors in forming …

Within the past few years, social media platforms such as Facebook, Twitter, and Sina
Weibo, have gradually become important channels for information dissemination and …

One of the main concerns for online shopping websites is to provide efficient and
customized recommendations to a very large number of users based on their preferences …

Trust-aware recommender systems are advanced approaches which have been developed
based on social information to provide relevant suggestions to users. These systems can …

With the development of personalized recommendations, information overload has been
alleviated. However, the sparsity of the user-item rating matrix and the weak transitivity of …