東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation./
作者:	Salman, Omar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	94 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
Contained By:	Dissertations Abstracts International83-04B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28648224
ISBN:	9798460415519

Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation.
Salman, Omar.

Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 94 p.

Source: Dissertations Abstracts International, Volume: 83-04, Section: B.

Thesis (Ph.D.)--University of Arkansas, 2021.

This item must not be sold to any third party vendors.

Expert recommendation is the process of identifying individuals who have the appropriate knowledge and skills to achieve a specific task. It has been widely used in the educational environment mainly in the hiring process, paper-reviewer assignment, and assembling conference program committees. In this research, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We introduce a novel way to model experts in academia by considering demographic attributes in addition to skills. We use the h-index score to quantify skills for a researcher and we identify five demographic features with which to represent a researcher's demographic profile. We highlight the importance of these features and their role in bias within the academic environment. We utilize these demographic features within an expert recommender system in academia to achieve demographic diversity and increase the exposure of the underrepresented groups using two approaches. In the first approach, we present three different algorithms for scholar recommendation: expertise-based, diversity-based, and a hybrid algorithm that uses a tuning parameter to calibrate the balance between expertise loss and diversity gain. To evaluate the ranking produced by these algorithms, we introduce a modified normalized Discounted Cumulative Gain (nDCG) version that supports multi-dimensional features, and we report diversity gain from each method. Our results show that we can achieve the best possible balance between diversity gain and expertise loss when the tuning parameter value is set around 0.4, giving nearly equal weight to both expertise and diversity. Finally, we explore diversity from the lens of the demographic parity and develop two algorithms to achieve a representative group that reflects the demographics of the recommendation pool. One is inspired by Hill Climbing, a mathematical optimization technique, wherein a solution is built gradually to the problem, and the other one is inspired by the problem of seat allocation in electoral voting systems. We evaluated these algorithms by comparing them to the hybrid algorithm from the previous approach. Our evaluation shows that both approaches provide a better diversity gain as compared to the hybrid algorithm. However, Hill Climbing Diversity is more effective when it comes to expertise savings with a statistically significant result, making it the preferred algorithm to achieve the goal of promoting diversity while maintaining expertise in an expert recommendation process.

ISBN: 9798460415519Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Promoting diversity

Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation.
LDR:03775nmm a2200337 4500 001 2343547
005 20220512065834.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798460415519
035 $a (MiAaPQ)AAI28648224
035 $a AAI28648224
040 $a MiAaPQ $c MiAaPQ
100 1 $a Salman, Omar. $0 (orcid)0000-0003-4797-6927 $3 3682130
245 1 0 $a Promoting Diversity in Academic Research Communities through Multivariate Expert Recommendation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 94 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
500 $a Advisor: Gauch, Susan.
502 $a Thesis (Ph.D.)--University of Arkansas, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Expert recommendation is the process of identifying individuals who have the appropriate knowledge and skills to achieve a specific task. It has been widely used in the educational environment mainly in the hiring process, paper-reviewer assignment, and assembling conference program committees. In this research, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We introduce a novel way to model experts in academia by considering demographic attributes in addition to skills. We use the h-index score to quantify skills for a researcher and we identify five demographic features with which to represent a researcher's demographic profile. We highlight the importance of these features and their role in bias within the academic environment. We utilize these demographic features within an expert recommender system in academia to achieve demographic diversity and increase the exposure of the underrepresented groups using two approaches. In the first approach, we present three different algorithms for scholar recommendation: expertise-based, diversity-based, and a hybrid algorithm that uses a tuning parameter to calibrate the balance between expertise loss and diversity gain. To evaluate the ranking produced by these algorithms, we introduce a modified normalized Discounted Cumulative Gain (nDCG) version that supports multi-dimensional features, and we report diversity gain from each method. Our results show that we can achieve the best possible balance between diversity gain and expertise loss when the tuning parameter value is set around 0.4, giving nearly equal weight to both expertise and diversity. Finally, we explore diversity from the lens of the demographic parity and develop two algorithms to achieve a representative group that reflects the demographics of the recommendation pool. One is inspired by Hill Climbing, a mathematical optimization technique, wherein a solution is built gradually to the problem, and the other one is inspired by the problem of seat allocation in electoral voting systems. We evaluated these algorithms by comparing them to the hybrid algorithm from the previous approach. Our evaluation shows that both approaches provide a better diversity gain as compared to the hybrid algorithm. However, Hill Climbing Diversity is more effective when it comes to expertise savings with a statistically significant result, making it the preferred algorithm to achieve the goal of promoting diversity while maintaining expertise in an expert recommendation process.
590 $a School code: 0011.
650 4 $a Computer science. $3 523869
650 4 $a Multicultural education. $3 526718
653 $a Promoting diversity
653 $a Academic research communities
653 $a Multivariate expert recommendation
690 $a 0984
690 $a 0455
710 2 $a University of Arkansas. $b Computer Engineering. $3 1672578
773 0 $t Dissertations Abstracts International $g 83-04B.
790 $a 0011
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28648224