With the evolution of social media, an enormous amount of information is shared every day. Recommender systems contribute significantly in handling big data and presenting relevant information, services and items to people. A substantial number of recommender system algorithms based on social media data have been proposed and applied to numerous domains in the literature. This paper presents a state-of-the-art survey of existing techniques of social recommender systems. We present different domains where the existing systems have been experimented. We also present a tabular representation of different metrics used by these papers. We discuss some frequently used datasets of these systems. Lastly, we discuss some of the future works in this area. The main aim of this paper is to provide a concise review of published papers to assist potential researchers in this field to devise new techniques.

中文翻译：

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社交推荐系统：技术、领域、指标、数据集和未来范围

随着社交媒体的发展，每天都有大量信息被共享。推荐系统在处理大数据和向人们展示相关信息、服务和项目方面做出了重大贡献。已经提出了大量基于社交媒体数据的推荐系统算法，并将其应用于文献中的众多领域。本文对社会推荐系统的现有技术进行了最先进的调查。我们展示了对现有系统进行试验的不同领域。我们还提供了这些论文使用的不同指标的表格表示。我们讨论了这些系统的一些常用数据集。最后，我们讨论了该领域的一些未来工作。