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 sea...

• Type: Conference
• Year: 2008
• Where: WWW
• Authors: Jae-wook Ahn, Peter Brusilovsky, Daqing He, Jonathan Grady, Qi Li
• Keywords: Internet Technology, Personalized Search, Traditional Search System, Web Search, WWW 2008