A Survey on Recommendation Methods Based on Social Relationships

Abstract

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers.

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

“…In these studies, we can find the use of very different information from social networks [16]. Social relationships are used jointly with user-items interaction in a method based on matrix factorization [27].…”

“…Although information from social relations has recently been widely exploited in recommender systems [16], there are very few papers in the literature that propose recommendation methods using homophily derived from social networks [17,18].…”