A Survey on Semantic-based Friend Recommendation System for Social Networks

Abstract

Friend book is a novel semantic-based friend recommendation system for social networks, based on their life styles instead of social graphs which recommend friends to users. Existing social networking services recommend friends to users based on their social graphs, which may not be the most appropriate to reflect a user’s preferences on friend selection in real life. User’s daily life is modeled as life documents, from which users life styles are extracted by using the Latent Dirichlet Allocation algorithm; Similarity metric to measure the similarity of life styles between users, user’s impact is calculated in terms of life styles with a friend-matching graph. In this paper, a social network is formally represented and taking text mining as a perspective, we have proposed a framework that will recommend friend using an efficient Algorithm. We find solution in proposed work,the factor of personal interest can make the recommend items to meet users' individualities based on keywords and location in searching. We suggest users while searching based on user’s search history. We process the search history based on keywords and location. Experimental results show the proposed approach outperforms the existing approaches.
Keywords:- Friend recommendation; life style; social networks