Privacy-preserving top-N recommendation on distributed data

Huseyin Polat, Wenliang Du

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Traditional collaborative filtering (CF) systems perform filtering tasks on existing databases; however, data collected for recommendation purposes may split between different online vendors. To generate better predictions, offer richer recommendation services, enhance mutual advantages, and overcome problems caused by inadequate data and/or sparseness, e-companies want to integrate their data. Due to privacy, legal, and financial reasons, however, they do not want to disclose their data to each other. Providing privacy measures is vital to accomplish distributed databased top-N recommendation (TN), while preserving data holders' privacy. In this article, the authors present schemes for binary ratings-based TN on distributed data (horizontally or vertically), and provide accurate referrals without greatly exposing data owners' privacy. Our schemes make it possible for online vendors, even competing companies, to collaborate and conduct TN with privacy, using the joint data while introducing reasonable overhead costs.

Original languageEnglish (US)
Pages (from-to)1093-1108
Number of pages16
JournalJournal of the American Society for Information Science and Technology
Issue number7
StatePublished - May 1 2008

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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