Ranking model construction is an important topic in information retrieval. Recently, many approaches based on the idea of ¡§learning to rank¡¨ have been proposed for this task and most of them attempt to score all documents of different queries by resorting to a single function. In this thesis, we propose a novel framework of query-dependent ranking. A simple similarity measure is used to calculate similarities between queries. An individual ranking model is constructed for each training query with corresponding documents. When a new query is asked, documents retrieved for the new query are ranked according to the scores determined by a ranking model which is combined from the models of similar training queries. A mechanism for determining combining weights is also provided. Experimental results show that this query dependent ranking approach is more effective than other approaches.