  The Web is a canonical example of a competitive retrieval setting where many
documents' authors consistently modify their documents to promote them in
rankings. We present an automatic method for quality-preserving modification of
document content -- i.e., maintaining content quality -- so that the document
is ranked higher for a query by a non-disclosed ranking function whose rankings
can be observed. The method replaces a passage in the document with some other
passage. To select the two passages, we use a learning-to-rank approach with a
bi-objective optimization criterion: rank promotion and content-quality
maintenance. We used the approach as a bot in content-based ranking
competitions. Analysis of the competitions demonstrates the merits of our
approach with respect to human content modifications in terms of rank
promotion, content-quality maintenance and relevance.
