1. A computer-implemented method, comprising: identifying, as positive images, a first set of images that are considered relevant to a first query based, at least in part, on a first selection rate for images in the first set when presented as search results for the first query; identifying, as negative images, a second set of images that differ from the first set of images and that are considered relevant to a different query based, at least in part, on a second selection rate for images in the second set when presented as search results for the different query; training an image relevance model for the first query based on feature values of the positive images and feature values of the negative images; generating a score for each of a plurality of images based on feature values of the plurality of images and the image relevance model; receiving a search query; identifying, from the plurality of images, one or more highest scoring images based on the score for each of one or more of the plurality of images; and providing at least a portion of the one or more highest scoring images as results for the search query.
5. The method of claim 1 , comprising generating multiple different image relevance models for a plurality of different queries, the generating of the multiple different image relevance models comprising: identifying a particular query that was submitted by a user; determining that the particular query differs from each query for which an image relevance model is available; and in response to the determination that the particular query differs from each query for which an image relevance model is available: identifying positive images that have at least a first minimum specified user interaction rate when presented in search results for the particular query; identifying negative images that have at least a second minimum specified user interaction rate when presented in search results for a query that differs from the particular query; and generating the image relevance model for the particular query based on the positive images and the negative images.