Abstract
The success of a search engine in cloud computing environment relies on the numbers of users and their click-through. If we take the previous search key words as tags of users to study and differentiate the user interaction behaviors, the search engine is able to actively push related and useful information to users based on their previous actions instead of passively waiting for users’ queries. However the user searching behavior is affected by lots of factors, and it is quite complex and uncertain. The log files provided by a search engine have recorded all the information of the user interaction process on their servers or browsers, such as key words, click-through rate, time stamp, time on page, IP address, browser type and system stats, even the user location etc, which are all important information to understand and categorize users’ searching behavior. Is there any statistical property almost independent to search key words? How to push recommendation based on the queried key words? And how to extract user behavior models of searching actions in order to recommend the information to meet users’ real needs more timely and precisely?
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© 2011 Springer-Verlag Berlin Heidelberg
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Li, D. (2011). iMiner: From Passive Searching to Active Pushing. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds) Web Technologies and Applications. APWeb 2011. Lecture Notes in Computer Science, vol 6612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20291-9_2
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DOI: https://doi.org/10.1007/978-3-642-20291-9_2
Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-20291-9
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