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 taskbased 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.
Personalization task-based information exploration adaptive search user profile task model empirical study
Jae-wook Ahn Peter Brusilovsky Daqing He Jonathan Grady Qi Li
School of Information Sciences, University of Pittsburgh 135 N. Bellefield Ave., Pittsburgh, PA 15256, USA
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）