25/07/2020

Ranking-incentivized quality preserving content modification

Gregory Goren, Oren Kurland, Moshe Tennenholtz, Fiana Raiber

Keywords: search engine optimization, adversarial retrieval

Abstract Paper Similar Papers

Abstract: 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.

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