A trust prediction approach by using collaborative filtering and computing similarity in social networks

Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and aren't able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Nowadays, trust prediction is not calculated with high accuracy. Collaborative filtering approach has become more applicable and is almost used in recommendation systems. In this approach, it is tried that users' rating of certain areas to be gathered and the similarity of users or items are measured, the most suitable and nearest item of user's preference will be realized and recommended. By using this concept and the most innovative and available approach to measure similarity is recommended to the target user. Then the trusted user is found. The results demonstrate that the recommended approach significantly improves the accuracy of trust prediction in social networks.