Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
User Modeling and User-Adapted Interaction
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
A Novel Approach for Frequent Phrase Mining in Web Search Engine Query Streams
CNSR '07 Proceedings of the Fifth Annual Conference on Communication Networks and Services Research
On Query Completion in Web Search Engines Based on Query Stream Mining
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Dynamic refinement of search engines results utilizing the user intervention
Journal of Systems and Software
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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.