Re-ranking Google search returned web documents using document classification scores
Suthira Plansangket, John Q. Gan · Artificial Intelligence Research · 2016
Web document ranking is a very challenging issue for search engines because about 80% of the search engine users are usuallyinterested in the top three returned search results only. This paper proposes an effective method for re-ranking Google searchreturned web documents/pages based on document classification. This method downgrades some web documents/pages thathave lower classification scores or been classified into categories irrelevant to the query. The experimental results show thatthe re-ranking of Google search returned web documents using document classification scores can significantly improve theranking performance in terms of the integrated evaluation result using three criteria: MAP, nDCG, and P@20. It is evident thatthe proposed re-ranking method can meet the user’s information need better.