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Authors: Guillaume Blot 1 ; Francis Rousseaux 2 and Pierre Saurel 1

Affiliations: 1 Paris-Sorbonne, France ; 2 Reims Champagne Ardenne University, France

ISBN: 978-989-758-274-5

Keyword(s): Recommender Engines, Link Prediction, Collaborative Filtering, Information Cascades, Confirmation Bias.

Abstract: Digital knowledge gave birth to massive communication spaces, new access paths and new cleavages. Our experiment deals with the challenging issue of accessing this knowledge on the Internet. Computer scientists set up prediction algorithms and recommender engines. This way, knowledge access is partly automatized. Using a real-life dataset, our goal is to simulate the iterative behavior shift produced by most used recommender engines. On this basis, we show that in the context of recommendation, existing evaluation metrics are driven by prediction testing methods and we argue that ambiguity has to be raised between prediction and recommendation. Secondly, we propose alternative evaluation metrics for recommendation systems, targeting mitigating the bias problem of information cascades and confirmation biases.

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Paper citation in several formats:
Blot, G.; Rousseaux, F. and Saurel, P. (2017). Challenging Recommendation Engines Evaluation Metrics and Mitigating Bias Problem of Information Cascades and Confirmation Biases.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 393-400. DOI: 10.5220/0006581803930400

@conference{ijcci17,
author={Guillaume Blot. and Francis Rousseaux. and Pierre Saurel.},
title={Challenging Recommendation Engines Evaluation Metrics and Mitigating Bias Problem of Information Cascades and Confirmation Biases},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={393-400},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006581803930400},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Challenging Recommendation Engines Evaluation Metrics and Mitigating Bias Problem of Information Cascades and Confirmation Biases
SN - 978-989-758-274-5
AU - Blot, G.
AU - Rousseaux, F.
AU - Saurel, P.
PY - 2017
SP - 393
EP - 400
DO - 10.5220/0006581803930400

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