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

Matrix factorization on user-item rating matrices has achieved significant success in
collaborative filtering based recommendation tasks. However, it also encounters the …

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

Matrix Approximation (MA) is a powerful technique in recommendation systems. There are
two main problems in the prevalent MA framework. First, the latent factor is out of …

Thanks to its successful application in recommendation systems, collaborative filtering (CF)
technique has become one of the most popular research topic in data mining and …

Matrix approximation is a common model-based approach to collaborative filtering in
recommender systems. Many relevant algorithms that fuse social contextual information …

Collaborative filtering (CF) technique plays an important role in generating personalized
recommendations, but its performance is challenged by the problems of data sparsity and …

Recently, some recommendation methods try to relieve the data sparsity problem of
Collaborative Filtering by exploiting data from users' multiple types of behaviors. However …

In this paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a social collaborative filtering …