A user-profile-based friendship recommendation solution in social networks

Today, OSNs (online social networks) have become a popular communication platform, and a wide range of applications are considered for them. One fundamental relationship among members of a social network is friendship. Friend matching is a reasonable way to suggest people to each other. In this paper, we propose a model for measuring the similarities of two members based on information contained in their profiles. Unlike other profile-matching models, we do not assign equal weights to different items in users' profiles to measure their similarity. In the first phase of the proposed scheme, we develop a mining model to discover the actual degree of influence of different factors that affect the formation of friendships. We extract this information from a real online social network. Then, based on the analysis results of this large dataset, matching and recommender systems are designed. The experimental results are encouraging and show that the proposed model significantly outperforms its counterparts.