Recommender Systems for Online and Mobile Social Networks: A survey

TL;DR

This survey reviews recommender systems tailored for online and mobile social networks, emphasizing social context integration, distributed algorithm challenges, and future research directions.

Contribution

It provides a comprehensive overview of existing RS approaches for social networks, highlighting social context use and distributed system optimization.

Findings

Social context improves recommendation quality.

Distributed algorithms face unique challenges in social networks.

Open research issues include scalability and privacy.

Abstract

Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.