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Adaptive Visualization for Focused Personalized Information Retrieval

Ahn, Jae-wook (2010) Adaptive Visualization for Focused Personalized Information Retrieval. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The new trend on the Web has totally changed todays information access environment. The traditional information overload problem has evolved into the qualitative level beyond the quantitative growth. The mode of producing and consuming information is changing and we need a new paradigm for accessing information.Personalized search is one of the most promising answers to this problem. However, it still follows the old interaction model and representation method of classic information retrieval approaches. This limitation can harm the potential of personalized search, with which users are intended to interact with the system, learn and investigate the problem, and collaborate with the system to reach the final goal.This dissertation proposes to incorporate interactive visualization into personalized search in order to overcome the limitation. By combining the personalized search and the interac- tive visualization, we expect our approach will be able to help users to better explore the information space and locate relevant information more efficiently.We extended a well-known visualization framework called VIBE (Visual Information Browsing Environment) and implemented Adaptive VIBE, so that it can fit into the per- sonalized searching environment. We tested the effectiveness of this adaptive visualization method and investigated its strengths and weaknesses by conducting a full-scale user study.We also tried to enrich the user models with named-entities considering the possibility that the traditional keyword-based user models could harm the effectiveness of the system in the context of interactive information retrieval.The results of the user study showed that the Adaptive VIBE could improve the precision of the personalized search system and could help the users to find out more diverse set of information. The named-entity based user model integrated into Adaptive VIBE showed improvements of precision of user annotations while maintaining the level of diverse discovery of information.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Ahn, Jae-wookjaa38@pitt.eduJAA38
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairBrusilovsky, Peterpeterb@pitt.eduPETERB
Committee MemberHe, Daqingdah44@pitt.eduDAH44
Committee MemberSpring, Michaelspring@pitt.eduSPRING
Committee MemberChristel,
Committee MemberLewis, Mikeml@sis.pitt.eduCMLEWIS
Date: 23 December 2010
Date Type: Completion
Defense Date: 8 September 2010
Approval Date: 23 December 2010
Submission Date: 26 August 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: adaptive visualization; information retrieval; personalization; personalized search
Other ID:, etd-08262010-150850
Date Deposited: 10 Nov 2011 20:01
Last Modified: 15 Nov 2016 13:49


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