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Authors: Mahsa Badami 1 ; Olfa Nasraoui 1 and Patrick Shafto 2

Affiliations: 1 Knowledge Discovery and Web Mining Lab, Computer Science and Computer Engineering Department, University of Louisville, Louisville, KY and U.S.A. ; 2 Department of Mathematics and Computer Science, Rutgers University - Newark, Newark, NJ and U.S.A.

ISBN: 978-989-758-330-8

Keyword(s): Recommender System, Polarization, Controversy, Big data, Algorithmic Bias.

Abstract: Personalized recommender systems are commonly used to filter information in social media, and recommendations are derived by training machine learning algorithms on these data. It is thus important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We investigate how filtering and discovering information are affected by using recommender systems. We study the phenomenon of polarization within the context of the users interactions with a space of items and how this affects recommender systems. We then investigate the behavior of machine learning algorithms in such environments. Finally we propose a new recommendation model based on Matrix Factorization for existing collaborative filtering recommender systems in order to combating over-specialization in polarized environments toward counteracting polarization in human-generated data and machine learning algorithms.

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Paper citation in several formats:
Badami, M.; Nasraoui, O. and Shafto, P. (2018). PrCP: Pre-recommendation Counter-Polarization.In Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, ISBN 978-989-758-330-8, pages 282-289. DOI: 10.5220/0006938702820289

@conference{kdir18,
author={Mahsa Badami. and Olfa Nasraoui. and Patrick Shafto.},
title={PrCP: Pre-recommendation Counter-Polarization},
booktitle={Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,},
year={2018},
pages={282-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006938702820289},
isbn={978-989-758-330-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,
TI - PrCP: Pre-recommendation Counter-Polarization
SN - 978-989-758-330-8
AU - Badami, M.
AU - Nasraoui, O.
AU - Shafto, P.
PY - 2018
SP - 282
EP - 289
DO - 10.5220/0006938702820289

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