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Authors: Benjamin Gras ; Armelle Brun and Anne Boyer

Affiliation: Université de Lorraine - LORIA, France

ISBN: 978-989-758-246-2

ISSN: 2184-3252

Keyword(s): Recommender Systems, Grey Sheep Users, Matrix Factorization.

Related Ontology Subjects/Areas/Topics: Enterprise Information Systems ; Internet Technology ; Recommendation Systems ; Software Agents and Internet Computing ; Web Information Systems and Technologies ; Web Services and Web Engineering

Abstract: Matrix Factorization (MF)-based recommender systems provide on average accurate recommendations, they do consistently fail on some users. The literature has shown that this can be explained by the characteristics of the preferences of these users, who only partially agree with others. These users are referred to as Grey Sheep Users (GSU). This paper studies if it is possible to design a MF-based recommender that improves the accuracy of the recommendations provided to GSU. We introduce three MF-based models that have the characteristic to focus on original ways to exploit the ratings of GSU during the training phase (by selecting, weighting, etc.). The experiments conducted on a state-of-the-art dataset show that it is actually possible to design a MF-based model that significantly improves the accuracy of the recommendations, for most of GSU.

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Paper citation in several formats:
Gras, B.; Brun, A. and Boyer, A. (2017). Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users?.In Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-246-2, ISSN 2184-3252, pages 88-96. DOI: 10.5220/0006302700880096

@conference{webist17,
author={Benjamin Gras. and Armelle Brun. and Anne Boyer.},
title={Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users?},
booktitle={Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2017},
pages={88-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006302700880096},
isbn={978-989-758-246-2},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users?
SN - 978-989-758-246-2
AU - Gras, B.
AU - Brun, A.
AU - Boyer, A.
PY - 2017
SP - 88
EP - 96
DO - 10.5220/0006302700880096

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