Ranking-Incentivized Document Manipulations for Multiple Queries

In competitive retrieval settings, document publishers (authors) modify their documents in response to induced rankings so as to potentially improve their future rankings. Previous work has focused on analyzing ranking-incentivized document modifications for a single query. We present a novel theoretical and empirical study of document modification strategies applied for improved ranking for multiple queries; e.g., those representing the same information need. Using game theoretic analysis, we show that in contrast to the single-query setting, an equilibrium does not necessarily exist. We empirically study document modification strategies in the multiple-queries setting by organizing ranking competitions. In contrast to previous ranking competitions devised for the single-query setting, we also used a neural ranker and allowed in some competitions the use of generative AI tools to modify documents. We found that publishers tend to mimic content from documents highly ranked in the past, as in the single-query setting, although this was a somewhat less emphasized trend when generative AI tools were allowed. We also demonstrate the merits of using information induced from multiple queries to predict which document might be the highest ranked in the next ranking for a given query.