Re-ranking search results using query logs

This work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get high ranks for an underspecified query due to their freshness and to the large number of pages that match the query, despite the fact that a large number of users have searched for parts of their content recently. We propose a novel method, Q-Rank, to effectively refine the ranking of search results for any given query by constructing the query context from search query logs. Evaluation results show that Q-Rank gains a considerable advantage over the current ranking system of a large-scale commercial Web search engine, being able to improve the relevance of search results for 82% of the queries.