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Social Recommender Systems
Social overload corresponds to being imposed to high amount of information and interaction on social web. Social overload causes some challenges from the aspect of both social media websites and their users. Users need to deal with high volume of information and to make decisions among different social network applications whereas social network sites try to keep their existing users and make their sites interesting to users. To overcome social overload, social recommender systems has been utilized to engage users in social media websites in a way that users receive more personalized content using recommendation techniques. Social recommender systems are specific types of recommendation systems being designed for social media and utilizing new sort of data brought by it, such as likes, comments, tags and so on, to improve effectiveness of recommendations. Recommendation in social media have several aspects like recommendation of social media content, people, groups and tags.

Social Media Content Recommendation
Social media lets users to provide feedback on the content produced by users of social media websites, by means of commenting on or liking the content shared by others and annotating their own-created content via tagging. This newly introduced metadata by social media helps to obtain recommendations for social media content with improved effectiveness. Also, social media lets to extract the explicit relationship between users such as friendship and people followed/followers. This provides further improvement on collaborative filtering systems because now users can have judgement on the recommendations provided based on the people they have relationships. There have been studies showing the effectiveness of recommendation systems which utilize relationships among users on social media compared to traditional collaborative filtering based systems, specifically for movie and book recommendation. Another improvement brought by social media to recommender systems is solving the cold start problem for new users.

Some key application areas of social media content recommendation are blog and blog post recommendation, multimedia content recommendation such as YouTube videos, question and answer recommendation to question askers and answerers on social question-and-answer websites, job recommendation (LinkedIn), news recommendation on social new aggregator sites (like Digg, GoogleReader, Reddit etc.), short message recommendations on microblogs (such as Twitter).

Social Media People Recommendation
Also known as social matching (the term is proposed by Terveen and McDonald), people recommender systems deal with recommending people to people on social media. Aspects making people recommender systems distinct from traditional recommender systems and require special attention are basically privacy, trust among users, and reputation. There are several factors which effect the choice of recommendation techniques for people recommendation on social networking sites (SNS). Those factors are related to types of relationships among people on social networking sites, such as symmetric vs asymmetric, ad-hoc vs long-term, and confirmed vs nonconfirmed relationships.

The scope of people recommender systems can be categorized into three : recommending familiar people to connect with, recommending people to follow and recommending strangers. Recommending strangers is seen as valuable as recommending familiar people because of leading to chances such as exchanging ideas, obtaining new opportunities, and increasing one’s reputation.

Challenges in Social Recommender Systems
Handling with social streams is one of the challenges social recommender systems face with. Social stream can be described as the user activity data pooled on newsfeed on social media websites. Social stream data has unique characteristics such as rapid flow, variety of data (only text content vs heterogenous content), and requiring freshness. Those unique properties of stream data compared to traditional social media data impose challenges on social recommender systems. Another challenge in social recommendation is performing cross-domain recommendation, as in traditional recommender systems. The reason is that social media websites in different domains include different information about users, and merging information within different contexts may not lead to useful recommendations. For example, using favorite recipes of users in one social media site may not be a reliable source of information to effective job recommendations for them.