Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm

Author:

Cakmak Mert Can1ORCID,Okeke Obianuju1ORCID,Onyepunuka Ugochukwu1ORCID,Spann Billy1ORCID,Agarwal Nitin1ORCID

Affiliation:

1. University of Arkansas at Little Rock, Little Rock, United States

Funder

U.S. National Science Foundation

U.S. Office of the Under Secretary of Defense for Research and Engineering

U.S. Office of Naval Research

U.S. Defense Advanced Research Projects Agency

U.S. Army Research Office

U.S. Air Force Research Laboratory

Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock

Australian Department of Defense Strategic Policy Grants Program (SPGP)

Publisher

ACM

Reference33 articles.

1. Detecting VoIP Floods Using the Hellinger Distance

2. Information retrieval using Hellinger distance and sqrt-cos similarity

3. O. Okeke, M. C. Cakmak, B. Spann, and N. Agarwal, "Examining Content and Emotion Bias in YouTube's Recommendation Algorithm," In the Proceedings of the Ninth International Conference on Human and Social Analytics (HUSO 2023), Barcelona, Spain: Copyright (c) IARIA, 2023, Mar. 2023, p. 15 to 20. ISBN: 978-1-68558-066-7.

4. ‘What is this corpus about?’: using topic modelling to explore a specialised corpus

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