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Refereed Papers
Track: Browsers and UI
Paper Title:
Personalized Web Exploration with Task Models
Authors:
- Jae-wook Ahn(University of Pittsburgh)
- Peter Brusilovsky(University of Pittsburgh)
- Daqing He(University of Pittsburgh)
- Jonathan Grady(University of Pittsburgh)
- Qi Li(University of Pittsburgh)
Abstract:
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.
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