Checco, A. orcid.org/0000-0002-0981-3409, Bianchi, G. and Leith, D.J. (2017) BLC: Private Matrix Factorization Recommenders via Automatic Group Learning. ACM Transactions on Privacy and Security, 20 (2). ISSN 2471-2566
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 ACM. This is an author produced version of a paper subsequently published in ACM Transactions on Privacy and Security. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Apr 2018 13:54 |
Last Modified: | 30 Jun 2020 08:00 |
Published Version: | https://doi.org/10.1145/3041760 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3041760 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129377 |