As an effective deep representation learning technique for graph data, graph convolutional
network (GCN) has recently been widely applied to obtain better embedding of vertex. The …

Exploiting social networks is expected to enhance the performance of recommender
systems when interaction information is sparse. Existing social recommendation models …

Traditional collaborative filtering based recommender systems generally suffer from the
interaction data sparsity problem. Therefore, social recommendation is proposed to mitigate …

The recommender system is of great significance to alleviate information overload. The rise
of online social networks leads to a promising direction—social recommendation. By …

In recent years, social recommendation is a research hotspot because it contains social
network information which can effectively solve the problem of data sparsity and cold start …

With the prevalence of online social platforms, social recommendation has emerged as a
promising direction that leverages the social network among users to enhance …

The recent boom in social networking services has prompted the research of
recommendation systems. The basic assumption behind these works was that" users' …

Collaborative Filtering (CF) is one of the most successful approaches for recommender
systems. With the emergence of online social networks, social recommendation has become …

Most graph convolutional network (GCN)-based social recommendation frameworks fuse
social links with user-item interactions to enrich user representations, which alleviate the …

Social recommendation which aims to leverage social connections among users to enhance
the recommendation performance. With the revival of deep learning techniques, many efforts …