10. One or more computer-storage media devices storing computer-useable instructions that, when used by a computing device, cause the computing device to perform a method for using multiple contextual signals to re-rank search results, the method comprising: receiving a set of non-contextual search results based on a search query received from a user; receiving contextual signals that provide user-specific features associated with the user, the contextual signals being received from a source other than the user; prior to communicating the set of non-contextual search results for presentation to the user, utilizing a machine-learning model that uses an algorithm to compare each of the user-specific features associated with the user to determine which of the user-specific features are more important in re-ranking documents in the set of non-contextual search results; re-ranking the documents in the set of non-contextual search results based on the comparison of the user-specific features to generate a set of contextual search results; receiving feedback from the user regarding the documents in the set of contextual search results; and communicating the feedback to the machine-learning model, wherein the machine-learning model utilizes the feedback in its comparison of each of the user-specific features in determining the importance and the relevance of the user-specific features in relation to future search queries received from the user and other users.
10. One or more computer-storage media devices storing computer-useable instructions that, when used by a computing device, cause the computing device to perform a method for using multiple contextual signals to re-rank search results, the method comprising: receiving a set of non-contextual search results based on a search query received from a user; receiving contextual signals that provide user-specific features associated with the user, the contextual signals being received from a source other than the user; prior to communicating the set of non-contextual search results for presentation to the user, utilizing a machine-learning model that uses an algorithm to compare each of the user-specific features associated with the user to determine which of the user-specific features are more important in re-ranking documents in the set of non-contextual search results; re-ranking the documents in the set of non-contextual search results based on the comparison of the user-specific features to generate a set of contextual search results; receiving feedback from the user regarding the documents in the set of contextual search results; and communicating the feedback to the machine-learning model, wherein the machine-learning model utilizes the feedback in its comparison of each of the user-specific features in determining the importance and the relevance of the user-specific features in relation to future search queries received from the user and other users. 12. The one or more computer-storage media devices of claim 10 , wherein the feedback indicates the user's preference for certain data sources over others, wherein the data sources include web documents, images, weather information, stock information, news articles, and videos.