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Incorporating Diversity in Academic Expert Recommendation

Authors:
Omar Salman
Susan Gauch
Mohammed Alqahatani
Mohammed Ibrahim
Reem Alsaffar

Keywords: Expert Recommendation; Diversity; Fairness; nDCG.

Abstract:
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, assembling conference program committees, etc. In this paper, we highlight the problem of diversity and fair representation of underrepresented groups in expertise recommendation, factors that current expertise recommendation systems rarely consider. We present a novel way to model experts in the academic setting by considering the 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 present three different algorithms for scholar recommendation: expertise-based, diversity-based, and a hybrid approach. To evaluate the ranking produced by these algorithms, we propose a modified normalized Discounted Cumulative Gain (nDCG) version that supports multi-dimensional features and we report the diversity gain from each method. We used a tuning parameter to calibrate the balance between expertise loss and diversity gain. Our results show that we can achieve the best diversity gain increase when the tuning parameter value is set around 0.4, giving nearly equal weight to both expertise and diversity.

Pages: 102 to 107

Copyright: Copyright (c) IARIA, 2020

Publication date: March 22, 2020

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-765-8

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020