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A framework for personalizing web search with concept-based user profiles

A Framework for Personalizing Web Search with Concept-Based User Pro les KENNETH WAI-TING LEUNG, DIK LUN LEE, WILFRED NG, and HING YUET FUNG, Hong Kong University of Science and Technology Personalized search is an important means to improve the performance of a search engine. In this article, we propose a framework that supports mining a user ™s conceptual preferences from users ™ clickthrough data resulting from Web search. The discovered preferences are utilized to adapt a search engine ™s ranking function. In this framework, an extended set of conceptual preferences was derived for a user based on the concepts extracted from the search results and the clickthrough data. Then, a concept-based user pro le (CUP) representing the user pro le as a concept ontology tree is generated. Finally, the CUP is input to a support vector machine (SVM) to learn a concept preference vector for adapting a personalized ranking function that reranks the search results. In order to achieve more ‚exible personalization, the framework allows a user to control the amount of speci c CUP ontology information to be exposed to the personalized search engine. We study various parameters, such as conceptual relationships and concept features, arising from http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Internet Technology (TOIT) Association for Computing Machinery

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