1. A computer-implemented method, comprising: determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises: determining that the content provided to the particular user during the current user session includes a hub page, the hub page being a page that has been identified as part of both a first context and a second context, the first context being different from the second context, and each of the first context and the second context being identified based at least in part on characteristics of the hub page; identifying previous user sessions that include requests for the hub page; obtaining a first metric for the first context, the first metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the first context; obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the second context; and determining that the user context is one of the first context or second context based on the first metric and the second metric; and receiving, during the user session and after determining the user context, a user search query from the particular user; and providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context.
1. A computer-implemented method, comprising: determining, for a current user session and by one or more computers, a user context that is indicative of a category of user interest for the user session, the determination being based on content provided to a particular user during the current user session, wherein determining the user context comprises: determining that the content provided to the particular user during the current user session includes a hub page, the hub page being a page that has been identified as part of both a first context and a second context, the first context being different from the second context, and each of the first context and the second context being identified based at least in part on characteristics of the hub page; identifying previous user sessions that include requests for the hub page; obtaining a first metric for the first context, the first metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the first context; obtaining a second metric for the second context, the second metric being calculated based on a frequency with which the previous user sessions that included requests for the hub page were determined to be part of the second context; and determining that the user context is one of the first context or second context based on the first metric and the second metric; and receiving, during the user session and after determining the user context, a user search query from the particular user; and providing, by one or more computers and in response to receiving the user search query, ranked search results for the user search query, the ranking being based at least in part on a first contextual click model for the user context. 7. The computer-implemented method of claim 1 , wherein the user context comprises one or more of a news context, a shopping context, a travel context or an educational context, wherein the news context is determined when it is identified that the user is navigating sites related to news content, the shopping context is determined when it is identified that the user is navigating sites related to shopping content, the travel context is determined when it is identified that the user is navigating travel content and the educational context is determined when it is identified that the user is navigating educational content; and wherein the first contextual click model associated with the user context is operable to increase the ranking of content related to the user context.