Ranking-Incentivized Quality Preserving Content Modification
This addresses the challenge for web document authors in competitive retrieval settings to improve rankings without degrading quality, though it is incremental as it builds on existing ranking and content modification techniques.
The authors tackled the problem of promoting documents in search rankings while preserving content quality, presenting an automatic method that uses a learning-to-rank approach with bi-objective optimization to replace passages, and demonstrated its merits in competitions with respect to rank promotion, content-quality maintenance, and relevance.
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.