A Frequency Mining-Based Algorithm for Re-ranking Web Search Engine Retrievals

Abstract. Conventional web search engines retrieve too many documents for the majority of the submitted queries; therefore, they possess a good recall, since there are far more pages than a user can look at. Precision; however, is a critical factor in these conditions, because the most related documents should be presented at the top of the list. In this paper, we propose an online page re-rank model which relies on the users’ clickthrough feedbacks as well as frequent phrases from the past queries. The method is compared with a similar page re-rank algorithm called I-SPY. The results show the efficiency of the proposed method in ranking the more related pages on top of the retrieved list while monitoring a smaller number of query phrases in a hit-matrix. Employing thirteen months of queries for the University of New Brunswick’s search engine, the hit-matrix in our algorithm was on average 30 times smaller, while it showed better performance with regards to the re-rank of web searc...

• Type: Conference
• Year: 2008
• Where: AI
• Authors: M. Barouni-Ebrahimi, Ebrahim Bagheri, Ali A. Ghorbani
• Keywords: AI 2008, Artificial Intelligence, Conventional Web Search, Page Re-rank, Web Search Engines