Friend recommendations in social networks using genetic algorithms and network topology

Social networking sites employ recommendation systems in contribution to providing better user experiences. The complexity in developing recommendation systems is largely due to the heterogeneous nature of social networks. This paper presents an approach to friend recommendation systems by using complex network theory, cognitive theory and a Pareto-optimal genetic algorithm in a two-step approach to provide quality, friend recommendations while simultaneously determining an individual´s perception of friendship. Our research emphasizes that by combining network topology and genetic algorithms, better recommendations can be achieved compared to each individual counterpart. We test our approach on 1,200 Facebook users in which we observe the combined method to outper form purely social or purely network-based approaches. Our preliminary results represent strong potential for developing link recommendation systems using this combined approach of personal interests and the underlying network.