Ranking-Incentivized Quality Preserving Content Modification

TL;DR

This paper introduces an automatic method for modifying web content to improve its ranking without compromising quality, using a learning-to-rank approach that balances rank promotion and content quality.

Contribution

The paper presents a novel automated technique for content modification that optimizes ranking improvements while preserving content quality, tested as a bot in ranking competitions.

Findings

Effective rank promotion compared to human modifications

Maintains content quality during rank optimization

Demonstrates success in competitive ranking scenarios

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.