This paper is concerned with how to make efficient use of social information to improve
recommendations. Most existing social recommender systems assume people share similar …

Abstract GNNs (Graph Neural Networks) use graph structure to make recommendations,
receiving more and more attention. Firstly, existing work focuses on aggregating social …

Recommender systems are crucial to alleviate the information overload problem in online
worlds. Most of the modern recommender systems capture users' preference towards items …

Social recommendation, which utilizes social relations to enhance recommender systems,
has been gaining increasing attention recently with the rapid development of online social …

Collaborative filtering is a widely used technique for providing personalized
recommendations to users. However, traditional collaborative filtering methods fail to …

Content recommendation in social networks poses the complex problem of learning user
preferences from a rich and complex set of interactions (eg, likes, comments and tags for …

Recommender systems with social networks have been well studied in recent years.
However, most of these methods ignore the social contextual information among users and …

Collaborative filtering (CF) is one of the most popular techniques for building recommender
systems. To alleviate the data sparsity issue in CF, social recommendation has emerged by …

Matrix Factorization (MF) is a very popular method for recommendation systems. It assumes
that the underneath rating matrix is low-rank. However, this assumption can be too restrictive …

This paper examines the problem of social collaborative filtering (CF) to recommend items of
interest to users in a social network setting. Unlike standard CF algorithms using relatively …