Recent years are witnessing the rapid development of social recommendation to improve
the performance of recommender system, especially for cold start problem. Many of the …

Collaborative filtering (CF) is one of the most popular techniques for building recommender
systems. To overcome the data sparsity in CF, social recommender systems have emerged …

Social recommendation leverages social information to solve data sparsity and cold-start
problems in traditional collaborative filtering methods. However, most existing models …

To deal with the inherent data sparsity and cold-start problem, many recommender systems
try to exploit the textual information for improving prediction accuracy. Due to the significant …

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 …

Generating recommendations based on user-item interactions and user-user social relations
is a common use case in web-based systems. These connections can be naturally …

Most of the existing recommender systems understand the preference level of users based
on user-item interaction ratings. Rating-based recommendation systems mostly ignore …

Social recommendation task aims to predict users' preferences over items with the
incorporation of social connections among users, so as to alleviate the sparse issue of …

Social-based recommender systems have been recently proposed by incorporating social
relations of users to alleviate sparsity issue of user-to-item rating data and to improve …