Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we

propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In

addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using

data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.

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