Trust-Enhanced Recommendation of Friends in Web Based Social Networks Using Genetic Algorithms to Learn User Preferences

Abstract

Web-based social networks (WBSNs) are a promising new paradigm for large scale distributed data management and collective intelligences. But the exponential growth of social networks poses a new challenge and presents opportunities for recommender systems, such as complicated nature of human to human interaction which comes into play while recommending people. Web based recommender systems (RSs) are the most notable application of the web personalization to deal with problem of information overload. In this paper, we present a Friend RS for WBSNs. Our contribution is three fold. First, we have identified appropriate attributes in a user profile and suggest suitable similarity computation formulae. Second, a real-valued Genetic algorithm is used to learn user preferences based on comparison of individual features to increase recommendation effectiveness. Finally, inorder to alleviate the sparsity problem of collaborative filtering, we have employed trust propagation techniques. Experimental results clearly demonstrate the effectiveness of our proposed schemes.