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

Benjamin Gras 1 Armelle Brun 1 Anne Boyer 1
1 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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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Communication dans un congrès
13th International Conference on Web Information Systems and Technologies (WEBIST), Apr 2017, Porto, Portugal. pp.88 - 96, 2017, 〈10.5220/0006302700880096〉
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Contributeur : Armelle Brun <>
Soumis le : mercredi 26 juillet 2017 - 10:41:16
Dernière modification le : mardi 24 avril 2018 - 13:34:39

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Benjamin Gras, Armelle Brun, Anne Boyer. Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users?. 13th International Conference on Web Information Systems and Technologies (WEBIST), Apr 2017, Porto, Portugal. pp.88 - 96, 2017, 〈10.5220/0006302700880096〉. 〈hal-01569004〉

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