User Profile Relationships using String Similarity Metrics in Social Networks

Abstract: This article reviews the problem of degree of closeness and interaction level in a social network by ranking users based on similarity score. This similarity is measured on the basis of social, geographic, educational, professional, shared interests, pages liked, mutual interested groups or communities and mutual friends. The technique addresses the problem of matching user profiles in its globality by providing a suitable matching framework able to consider all profiles' attributes and finding the similarity by new ways of string metrics. It is able to discover the biggest possible number of profiles that are similar to the target user profile, which the existing techniques are unable to detect. Attributes were assigned weights manually; string and semantic similarity metrics were used to compare attributes values thus predicting the most similar profiles. Profile based similarity show the exact relationship between users and this similarity between user profiles reflects closeness and interaction between users.