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The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses

  • Ludovico BorattoEmail author
  • Gianni Fenu
  • Mirko Marras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11437)

Abstract

Most recommender systems are evaluated on how they accurately predict user ratings. However, individuals use them for more than an anticipation of their preferences. The literature demonstrated that some recommendation algorithms achieve good prediction accuracy, but suffer from popularity bias. Other algorithms generate an item category bias due to unbalanced rating distributions across categories. These effects have been widely analyzed in the context of books, movies, music, and tourism, but contrasting conclusions have been reached so far. In this paper, we explore how recommender systems work in the context of massive open online courses, going beyond prediction accuracy. To this end, we compared existing algorithms and their recommended lists against biases related to course popularity, catalog coverage, and course category popularity. Our study remarks even more the need of better understanding how recommenders react against bias in diverse contexts.

Keywords

Recommendation Algorithmic bias Learning Analytics 

Notes

Acknowledgments

Mirko Marras gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014–2020, Axis III “Education and Training”, TG 10, PoI 10ii, SG 10.5).

This work has been partially supported by the Italian Ministry of Education, University and Research under the programme “Smart Cities and Communities and Social Innovation” during “ILEARNTV, Anytime, Anywhere” Project (DD n.1937 05.06.2014, CUP F74G14000200008 F19G14000910008), and by the Agència per a la Competivitat de l’Empresa, ACCIÓ, under “AlgoFair” Project.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Data Science and Big Data Analytics UnitEURECATBarcelonaSpain
  2. 2.Department of Mathematics and Computer ScienceUniversity of CagliariCagliariItaly

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