Improving the accuracy and efficiency of tag recommendation system by applying hybrid methods

Recently applications of social tagging systems have increased. These systems allow users to organize, manage and search the required resource freely, thus by combination and integration of recommendation systems in social software, assisting users to appropriately assign tag to resources and try to improve annotation among users. The challenges of recommendation systems are large-scale data, inconsistence data, usage of time-consuming machine learning algorithms, long and unreasonable time of recommendation and not being scalable to the demands of real world applications. Recently more efforts have been conducted to solve these problems. In this paper we proposed a tag recommendation system that is able to work with large-scale data and being applied in real world. The proposed system's evaluation performed on a dataset collected from Delicious.com. The results demonstrated the efficiency and accuracy of proposed system.