Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users?

Benjamin Gras, Armelle Brun, Anne Boyer

2017

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 Harvard Style

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, pages 88-96. DOI: 10.5220/0006302700880096

in Bibtex Style

@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},
}


in EndNote Style

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