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.

翻译：推荐系统（RS）目前已成为在线服务中的基础工具，尤其是在在线社交网络（OSN）兴起的背景下。在此类场景中，用户生成海量内容，极易被无用信息淹没。与此同时，社交媒体作为表征内容与用户兴趣的重要信息来源，推荐系统可据此进一步个性化建议，优化推荐流程。本文针对面向在线及移动社交网络设计与实现的推荐系统进行了综述，重点阐释了社交情境信息如何提升推荐任务效能，以及标准算法需如何增强与优化以在完全分布式环境（如机会网络）中运行。我们从算法、目标领域、评估指标及性能评估维度阐述了这些系统的优劣，最终提出了该领域待解决的开放研究挑战。