1. A computer implemented method, comprising: identifying, as a training example for a query suggestion rule, a pair comprising a document visited and a subsequent query, the subsequent query submitted after visiting the document visited; generating a feature representation for the pair, the feature representation including a first vector of document features representing content of the document visited, and a second vector of query features representing content of the subsequent query; training the query suggestion rule, wherein training the query suggestion rule includes jointly embedding document features of the first vector and query features of the second vector in a reduced dimensionality space to induce distances between the embedded document features and the embedded query features in the reduced dimensionality space, wherein the distances are indicative of a semantic relationship between content of the document visited and content of the subsequent query; receiving a document locator corresponding to an additional document; generating a third vector of additional document features of the additional document, the additional document features representing the content of the additional document; mapping the third vector to the reduced dimensionality space; locating one or more embedded query features based on their respective distances from the mapped third vector; and identifying one or more suggested queries based on the located embedded query features.