Can Matrix Factorization Improve the Accuracy of Recommendations Provided to Grey Sheep Users? - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

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

(1) , (1) , (1)
1
Benjamin Gras
  • Function : Author
  • PersonId : 776377
  • IdRef : 225757850
Armelle Brun
  • Function : Author
  • PersonId : 776378
  • IdRef : 071349251
Anne Boyer

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.
Fichier principal
Vignette du fichier
Example.pdf (273.88 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01569004 , version 1 (26-07-2017)

Identifiers

Cite

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, ⟨10.5220/0006302700880096⟩. ⟨hal-01569004⟩
145 View
356 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More