document: Personalized web exploration with task models Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers . However , the majority of studies on personalized search focused on a rather simple type of search , which leaves an important research topic - the personalization in exploratory searches - as an under-studied area . In this paper , we present a study of personalization in task-based information exploration using a system called TaskSieve . TaskSieve is a Web search system that utilizes a relevance feedback based profile , called a `` task model '' , for personalization . Its innovations include flexible and user controlled integration of queries and task models , task-infused text snippet generation , and on-screen visualization of task models . Through an empirical study using human subjects conducting task-based exploration searches , we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system . TaskSieve helps users select significantly more accurate information for their tasks , allows the users to do so with higher productivity , and is viewed more favorably by subjects under several usability related characteristics .
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