Designing a Trust-Based Recommender System in Social Rating Networks

One of the most common styles of business today is electronic business, since it is considered as a principal means for financial transactions among advanced countries. In view of the fact that due to the evolution of human knowledge and the increase of expectations following that, traditional marketing in electronic business cannot meet current generation’s needs. In order to survive, organizations have to correct and even change their advertisement strategies along with changes, facts and behaviors of their customers. In order for this and considering unique characteristics of social networks in obtaining and evaluating customer’s behavior, there will a chance for the organization to reach its goals through performing such a business on social networks. In this article, a trust-based recommender system in Social Rating Networks (SRN) is designed in order to recommend an article to the user through receiving user ID and evaluation of related characteristics. A big real data set, Epinions, is used in the database of this system. According to the evaluation performed in this system, 99.01% of users studied the articles recommended by system and an average ratio of articles studied by each test sample to the recommended articles was 87.57%.