A Profile-based Friend Detection Scheme for Online Social Networks

In past few years, users created friends with every other World Health Organization live or work near them, like neighbor or work place etc. this kind of friendly relationship is named as ancient means of constructing friends or G. Friend referred to as geographical primarily based friends .Now social networking services recommend friends to users based on their social graphs, which may not be the most accurate in friend selection. An efficient friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social graphs. It can be applicable in smart phones, discovers life styles of users from user-centric sensor data, measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. We adopt Latent Dirichlet Allocation algorithm for lifestyle extraction. Similarity metric to measure the similarity of life styles between users, and calculate user’s Impact in terms of life styles. Also have a feedback mechanism to improve recommendation accuracy. In this paper, we propose a trust-based friend recommendation scheme for OSNs, where OSN users apply their attributes to find matched friends, and establish social relationships with strangers via a multi-hop trust chain. Based on trace-driven experimental results and security analysis.