document: Clustering query refinements by user intent We address the problem of clustering the refinements of a user search query . The clusters computed by our proposed algorithm can be used to improve the selection and placement of the query suggestions proposed by a search engine , and can also serve to summarize the different aspects of information relevant to the original user query . Our algorithm clusters refinements based on their likely underlying user intents by combining document click and session co-occurrence information . At its core , our algorithm operates by performing multiple random walks on a Markov graph that approximates user search behavior . A user study performed on top search engine queries shows that our clusters are rated better than corresponding clusters computed using approaches that use only document click or only sessions co-occurrence information .
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