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Automated question triage for social...

Park, Jong Do.

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Automated question triage for social reference: A study of adopting decision factors from digital reference.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Automated question triage for social reference: A study of adopting decision factors from digital reference./
Author:	Park, Jong Do.
Description:	179 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-12(E), Section: A.
Contained By:	Dissertation Abstracts International74-12A(E).
Subject:	Library Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3573174
ISBN:	9781303428975

Automated question triage for social reference: A study of adopting decision factors from digital reference.
Park, Jong Do.

Automated question triage for social reference: A study of adopting decision factors from digital reference. - 179 p.

Source: Dissertation Abstracts International, Volume: 74-12(E), Section: A.

Thesis (Ph.D.)--University of Pittsburgh, 2013.

The increasing popularity of Social Reference (SR) services has enabled a corresponding growth in the number of users engaging in them as well as in the number of questions submitted to the services. However, the efficiency and quality of the services are being challenged because a large quantity of the questions have not been answered or satisfied for quite a long time. In this dissertation project, I propose using expert finding techniques to construct an automated Question Triage (QT) approach to resolve this problem. QT has been established in Digital Reference (DR) for some time, but it is not available in SR. This means designing an automated QT mechanism for SR is very innovative.

ISBN: 9781303428975Subjects--Topical Terms:

881164
Library Science.

Automated question triage for social reference: A study of adopting decision factors from digital reference.
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100 1 $a Park, Jong Do. $3 2100238
245 1 0 $a Automated question triage for social reference: A study of adopting decision factors from digital reference.
300 $a 179 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-12(E), Section: A.
500 $a Adviser: Daqing He.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2013.
520 $a The increasing popularity of Social Reference (SR) services has enabled a corresponding growth in the number of users engaging in them as well as in the number of questions submitted to the services. However, the efficiency and quality of the services are being challenged because a large quantity of the questions have not been answered or satisfied for quite a long time. In this dissertation project, I propose using expert finding techniques to construct an automated Question Triage (QT) approach to resolve this problem. QT has been established in Digital Reference (DR) for some time, but it is not available in SR. This means designing an automated QT mechanism for SR is very innovative.
520 $a In this project, I first examined important factors affecting triage decisions in DR, and extended this to the SR setting by investigating important factors affecting the decision making of QT in the SR setting. The study was conducted using question-answer pairs collected from Ask Metafilter, a popular SR site. For the evaluation, logistic regression analyses were conducted to examine which factors would significantly affect the performance of predicting relevant answerers to questions.
520 $a The study results showed that the user's answering activity is the most important factor affecting the triage decision of SR, followed by the user's general performance in providing good answers and the degree of their interest in the question topic. The proposed algorithm, implementing these factors for identifying appropriate answerers to the given question, increased the performance of automated QT above the baseline for estimating relevant answerers to questions.
520 $a The results of the current study have important implications for research and practice in automated QT for SR. Furthermore, the results will offer insights into designing user-participatory DR systems.
590 $a School code: 0178.
650 4 $a Library Science. $3 881164
650 4 $a Information Science. $3 1017528
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690 $a 0723
710 2 $a University of Pittsburgh. $b Library and Information Science. $3 2100239
773 0 $t Dissertation Abstracts International $g 74-12A(E).
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791 $a Ph.D.
792 $a 2013
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