Recently, social tag recommendation has gained more attention in web research, and many approaches were proposed, which can be classified into two types: rule-based and classification-based approaches. However, too much expert experience and manual work are needed in rule-based approaches, and its generalization is limited. Additionally, there are some essential barriers in classification-based approaches, since tag recommendation is transformed into a multi-classes classification problem, such as tag collection is not fixed. Different from them, ranking model is more suitable, in which supervised learning can be used. In additions, the whole tag recommendation task can be divided into 4 subtasks according to the existence of users and resources. In different subtasks, different features are constructed, in order that existed information can be used sufficiently. The experimental results show that the proposed supervised ranking model performs well on the training and test dataset of RSDC 2008 recovered by ourselves.