Supervised ranking approaches have been becoming more and more important in the fields of information retrieval and machine learning. In ranking for document retrieval, queries often vary greatly from one to another. Only the documents retrieved from the same query are to be ranked against each other. However, in most of the existing approaches, losses from different queries are defined as the same. The significant diversities existing among queries are taken into consideration, and a rank aggregation framework for multiple dependent queries is proposed. This framework contains two steps, training of base rankers and query-level aggregation. Training of base ranker sets up a number of query-dependent base rankers based on each query and its relevant documents, and then turns the output of base rankers into feature vectors. Query-level aggregation uses a supervised approach to learn query-dependent weights when these base rankers are combined. As a case study, an SVM based model is employed to aggregate the base rankers, referred to as Q.D.RSVM. It is proved that Q.D.RSVM can set up query-dependent weights for different base rankers. Q.D.RSVM is applied to document retrieval and Web retrieval tasks. Experimental results based on benchmark datasets show that Q.D.RSVM outperforms conventional ranking approaches.