Improving recommender systems accuracy in social networks using popularity

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.