1. A system, comprising: one or more processors configured to: determine a plurality of sample pairs corresponding to a query word, wherein a sample pair of the plurality of sample pairs is associated with a first product and a second product responsive to the query word, wherein one of the first product and the second product is assigned a higher ranking priority than the other one of the first product and the second product based at least in part on historical user action data associated with the query word, wherein to determine the plurality of sample pairs corresponding to the query word includes to: determine a plurality of products responsive to the query word based at least in part on the historical user action data associated with the query word; determine a historical metric value for each product of the plurality of products based at least in part on the historical user action data associated with the query word; determine a plurality of pairs of products from the plurality of products; determine the plurality of sample pairs from the plurality of pairs of products, wherein each of the plurality of sample pairs meets a condition associated with the historical metric value; and assign a higher priority to a product in each sample pair of the plurality of sample pairs based at least in part on respective historical metric values corresponding to the two products of the sample pair; determine a plurality of feature score values corresponding to a set of ranking features associated with the query word for each product associated with each of at least a subset of the plurality of sample pairs; determine a set of weightings corresponding to the set of ranking features based at least in part on the plurality of feature score values and ranking priority assignments corresponding to the at least subset of the plurality of sample pairs, wherein to determine the set of weightings corresponding to the set of ranking features includes to train a machine learning model based at least in part on the plurality of feature score values and indications of higher ranking priority or lower ranking priority corresponding to the two products of each sample pair of the plurality of sample pairs; receive a subsequent search request including the query word; rank at least the plurality of products responsive to the query word based at least in part on the set of weightings; and output one or more ranked product search results; and one or more memories coupled to the one or more processors and configured to provide instructions to the one or more processors.