\label{sec:content} Social media introduced many new types of content that can be created and shared by any user in a way that has never been possible before. Users became the center of every social media website and in many cases were the ones creating the actual content of the site: textual content as in Wikipedia and WordPress; photos as in Flickr and Facebook; and video as in YouTube. Users also have a key role in providing feedback and annotating exiting content on social media websites. Comments allow users to add their own opinion; Votes and ratings allow them to ‘like’ (or dislike) favourite posts; and tags allow them to annotate the content with keywords that reflect their own viewpoint. These new types of feedback forms allow RS to implicitly infer user preferences and content popularity by analyzing the crowd’s feedback.
In the social media era, articulated relationships have become available through social network sites (SNSs) \cite{ellison2007social} and changed the world of content recommendation. While in the past such relationships could only be partially extracted by surveys and interviews, and later by mining communication patterns from phone logs or email that are highly sensitive privacy-wise, the availability of relationships in social networks allows tapping into one’s network of familiar people (Facebook, LinkedIn) or people of interest (Twitter) in a simpler way without infringing privacy. The use of the friend list instead of or alongside the list of similar people as in traditional CF has been broadly proven to be productive for enhancing content recommendations. Sinha and Swearingen \cite{sinha2001comparing} were among the first to compare friend-based recommendation with traditional methods and showed their effectiveness for movie and book recommendation. Golbeck \cite{golbeck2006generating} showed that friends can be a trusted source for movie recommendations. Groh and Ehmig \cite{Groh_2007} compared collaborative filtering with friend-based “social filtering” and showed the advantage of the latter for club recommendation within a German SNS. Overall, recommendation based on friends enhance recommendations’ accuracy; allow the user to better judge the recommendations since s/he is familiar with the respective people; spare the need for explicit feedback from the user in order to calculate similarity; and can be used for coping with the cold-start problem for new users.