Recommending Friends in Social Networks By Users' Profiles And Using Classification Algorithms

Nowadays, social networks are becoming more popular, so the number of their users and their information is growing accordingly. Therefore, we need a recommender system that uses all kinds of available information to create highly accurate recommendations. Regarding the general structure of these recommender systems, one criterion is first chosen to calculate the similarity between users and then people who are assumed to have great similarity are proposed to each other as friend. These similar criteria can calculate users’ similarity with regard to topological structure and some properties of graph vertices. In this paper, the properties that are required for clustering are extracted from users’ profile. Finally, by combining the similarity criteria of mean measure of divergence (MMD), cosine, and Katz, different aspects of the problem including graph topology, frequency of user interaction with each other, and normalization of the same scoring method are considered.