论文标题
排名引起的质量保留内容修改
Ranking-Incentivized Quality Preserving Content Modification
论文作者
论文摘要
网络是竞争检索环境的规范示例，许多文档的作者始终将其文档修改以在排名中促进其文档。我们提出了一种自动提供文档内容修改（即维持内容质量）的自动方法，以便可以通过观察到的排名的非公开排名函数对查询的查询排名更高。该方法用其他一些段落替换文档中的段落。为了选择这两个段落，我们使用双目标优化标准使用学习级别的方法：等级促进和内容质量维护。我们将该方法用作基于内容的排名竞赛的机器人。对竞争的分析证明了我们方法在人类内容修改方面的优点在等级促进，内容质量维护和相关性方面。
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.
