1. A computer-implemented method, comprising: generating a generic relevance function based on training data from a plurality of first users and that is not based on a specific context associated with any of the plurality of first users; storing the generic relevance function in a machine-readable storage medium; collecting context-specific training data, wherein the context-specific training data is based on a plurality of second users and a specific context associated with the plurality of second users; adapting the generic relevance function to produce a context-specific relevance function, wherein the adapting comprises using the generic relevance function and the context-specific training data as input to a machine learning technique to generate the context-specific relevance function; after producing the context-specific relevance function, receiving a query from a particular user; processing the query to identify results of the query; identifying a particular context of the query or of the particular user; selecting, based on the particular context, a particular context-specific relevance function from among a plurality of context-specific relevance functions; using the particular context-specific relevance function to determine relevance of each of the results only in response to determining that the particular context is the same as the specific context upon which the particular context-specific relevance function is based; based on the particular context-specific relevance function, assigning a relevance value to each of the results; and sending, to the particular user, at least a subset of the results to be displayed; wherein the method is performed by one or more computing devices.
1. A computer-implemented method, comprising: generating a generic relevance function based on training data from a plurality of first users and that is not based on a specific context associated with any of the plurality of first users; storing the generic relevance function in a machine-readable storage medium; collecting context-specific training data, wherein the context-specific training data is based on a plurality of second users and a specific context associated with the plurality of second users; adapting the generic relevance function to produce a context-specific relevance function, wherein the adapting comprises using the generic relevance function and the context-specific training data as input to a machine learning technique to generate the context-specific relevance function; after producing the context-specific relevance function, receiving a query from a particular user; processing the query to identify results of the query; identifying a particular context of the query or of the particular user; selecting, based on the particular context, a particular context-specific relevance function from among a plurality of context-specific relevance functions; using the particular context-specific relevance function to determine relevance of each of the results only in response to determining that the particular context is the same as the specific context upon which the particular context-specific relevance function is based; based on the particular context-specific relevance function, assigning a relevance value to each of the results; and sending, to the particular user, at least a subset of the results to be displayed; wherein the method is performed by one or more computing devices. 4. The method of claim 1 , wherein the specific context indicates one or more characteristics that are shared by the plurality of second users.