FriendBook+: An Activity Based Friend Recommendation System for Social Networks

Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a users preference on friend selection in real life. This paper present Friendbook a novel semantic-based friend recommendation system for social networks, which recommends friends to users based on their life styles and also depend on how active the users are on social networks. This paper also considers chat for recommending friends. Here users daily life as model as life documents, from which his/her life styles are extracted by using the Latent Dirichlet Allocation algorithm. After that the paper propose a similarity metric to measure the similarity of life style between users. Finally it returns a list of people with highest recommendation.