Privacy-preserving top-N recommendation on horizontally partitioned data

Huseyin Polat, Wenliang Du

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

Collaborative filtering techniques are widely used by many E-commerce sites for recommendation purposes. Such techniques help customers by suggesting products to purchase using other users' preferences. Today's top-N recommendation schemes are based on market basket data, which shows whether a customer bought an item or not. Data collected for recommendation purposes might be split between different parties. To provide better referrals and increase mutual advantages, such parties might want to share data. Due to privacy concerns, however, they do not want to disclose data. This paper presents a scheme for binary ratings-based top-N recommendation on horizontally partitioned data, in which two parties own disjoint sets of users' ratings for the same items while preserving data owners' privacy. If data owners want to produce referrals using the combined data while preserving their privacy, we propose a scheme to provide accurate top-N recommendations without exposing data owners' privacy. We conducted various experiments to evaluate our scheme and analyzed how different factors affect the performance using the experiment results.

Original languageEnglish (US)
Title of host publicationProceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005
Pages725-731
Number of pages7
DOIs
StatePublished - Dec 1 2005
Event2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005 - Compiegne Cedex, France
Duration: Sep 19 2005Sep 22 2005

Publication series

NameProceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005
Volume2005

Other

Other2005 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2005
CountryFrance
CityCompiegne Cedex,
Period9/19/059/22/05

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Privacy-preserving top-N recommendation on horizontally partitioned data'. Together they form a unique fingerprint.

  • Cite this

    Polat, H., & Du, W. (2005). Privacy-preserving top-N recommendation on horizontally partitioned data. In Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005 (pp. 725-731). [1517942] (Proceedings - 2005 IEEE/WIC/ACM InternationalConference on Web Intelligence, WI 2005; Vol. 2005). https://doi.org/10.1109/WI.2005.117