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Spying Out Real User Preferences for Metasearch Engine Personalization

Most current metasearch engines provide uniform service to users but do not cater for the specific needs of individual users. To address this problem, research has been done on personalizing a metasearch engine. An interesting and practical approach is to optimize its ranking function using clickthrough data. However, it is still challenging to infer accurate user preferences from the clickthrough data. In this paper, we propose a novel learning technique called “Spy Na¨ıve Bayes” (SpyNB) to identify...

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