Recommending Friend in Social Networks Based On Semantic

Abstract

Activity based friend recommendation services. Social networking sites imply friend recommendation Systems in contribution to providing better user experiences. Online friend recommendation is a rapid developing topic in web mining. Current social networking servicing recommend friends to users based on their social graphs and mutual friends , which may not be the most appropriate to reflect a user’s taste on friend selection in real lifetime . In this paper propose a system that recommends friends based on the daily activities of users. Here a semantic based friend recommendation is done based on the users’ life styles. By using text mining, we display a user's everyday life as life archives, from which his/her ways of life are separated by using the Latent Dirichlet Allocation algorithm. At that point we discover a similarity metric to quantify the similarity of life styles between users, and as certain users’ effect as far as ways of life with a similarity matching diagram. At last, we incorporate a feedback component to further enhance the proposal precision.