We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of “hiding in the crowd” privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or “nym”) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy.
Checco, A., Bianchi, G., & Leith, D.J. (2017). BLC: Private matrix factorization recommenders via automatic group learning. ACM TRANSACTIONS ON PRIVACY AND SECURITY, 20(2), 1-25.
Tipologia: | Articolo su rivista |
Citazione: | Checco, A., Bianchi, G., & Leith, D.J. (2017). BLC: Private matrix factorization recommenders via automatic group learning. ACM TRANSACTIONS ON PRIVACY AND SECURITY, 20(2), 1-25. |
Lingua: | English |
Settore Scientifico Disciplinare: | Settore ING-INF/03 |
Revisione (peer review): | Esperti anonimi |
Tipo: | Articolo |
Rilevanza: | Rilevanza internazionale |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1145/3041760 |
Stato di pubblicazione: | Pubblicato |
Data di pubblicazione: | 2017 |
Titolo: | BLC: Private matrix factorization recommenders via automatic group learning |
Autori: | |
Autori: | Checco, A; Bianchi, G; Leith, DJ |
Appare nelle tipologie: | 01 - Articolo su rivista |