A Frequency Mining-Based Algorithm for Re-ranking Web Search Engine Retrievals
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 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 search results. The proposed re-rank method is expandable to support user community-based searches as well as specific domain Web search engines.
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