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. 6. The computer-implemented method of claim 1 , further comprising generating a plurality of contextual click models for respective search queries associated with the user context, each contextual click model being operable to provide weightings for search results for respective search queries, and wherein the first contextual click model is one of the plurality of contextual click models.