{"id":158381,"date":"2009-11-01T00:00:00","date_gmt":"2009-11-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/clustering-queries-for-better-document-ranking\/"},"modified":"2018-10-16T20:19:47","modified_gmt":"2018-10-17T03:19:47","slug":"clustering-queries-for-better-document-ranking","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/clustering-queries-for-better-document-ranking\/","title":{"rendered":"Clustering Queries for Better Document Ranking"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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