Interfacing thought: cognitive aspects of human-computer interaction
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Clustering transactions using large items
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Privacy-preserving data mining
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Proceedings of the 11th international conference on World Wide Web
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Learning implicit user interest hierarchy for context in personalization
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Limiting privacy breaches in privacy preserving data mining
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Adaptive web search based on user profile constructed without any effort from users
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CubeSVD: a novel approach to personalized Web search
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A personalized search engine based on web-snippet hierarchical clustering
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Computer Communications
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Using social networks to distort users' profiles generated by web search engines
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Computer Standards & Interfaces
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DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
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WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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Information Sciences: an International Journal
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Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user.s interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.