Friend recommendation system in online social networks

Social media provide an important source of information regarding users and their interactions which is very valuable for the recommender systems. In web-based social networks social trust relationships between users indicate the similarity of their needs and opinions. In our paper, we presented a social network based recommender system app that utilizes the information of user and makes recommendations by considering users weight however we measured how many mutual friend they have been make suggesting and calculating the weight between each user by same formula and rule, then make recommending friends. We also help the users in a way by searching and recommending friends who do not belong to the same category of the major interest as the user but they have many mutual friends but they are not friends. Although there has been much work done in the industry and academia on developing the theory and application of social networks as well as recommender systems, the relation between these research areas is still unclear. An innovative idea, which enables to integrate these areas, and applies recommendation systems to the online social network systems, is proposed in this thesis. Recommendation systems for social networks differ from the typical kinds of recommendation solutions, since they suggest human beings to other ones rather than inanimate goods. Thus, conventional recommendation methods should be enhanced by social features of the networks and their members.