Social Friend Recommendation Based on Multiple Network Correlation,Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or “friend’s friends are friends,” which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR).

Social networks have experienced explosive growth in the last decade. Social websites such as Twitter, YouTube and Flickr have billions of users who share opinions, photos and videos every day.

Users make on-line friends through these social networks. One challenging issue is how to help these users to efficiently find new social friends.

Social friend recommendation has therefore become a new research topic and several methods have been proposed to conduct recommendation efficiently.

Social environment, including where one lives and works.Social behaviours and actions, including one’s working performance, shopping habits, hobbies, and, importantly, interactions with one another.Social status, such as gender, age, position, etc. We summarize all these aspects as an individual’s “social role”

Here the term “social role” is the part that a person plays as a member of a particular society. As stated : “In on-line social networks, people behave differently in social situations because they carry different latent social roles”.For example, a father and a child will respond differently when seeing a toy in a showcase at a shop. We believe that utilizing the individual’s social role information is a new research component for recommendation tasks.