SVD-based collaborative filtering with privacy

Huseyin Polat, Wenliang Du

Research output: Contribution to conferencePaperpeer-review

160 Scopus citations


Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques recommend products to customers using similar users' preference data. The performance of CF systems degrades with increasing number of customers and products. To reduce the dimensionality of filtering databases and to improve the performance, Singular Value Decomposition (SVD) is applied for CF. Although filtering systems are widely used by E-commerce sites, they fail to protect users' privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from customers is not an easy task. CF systems using these data might produce inaccurate recommendations. In this paper, we discuss SVD-based CF with privacy. To protect users' privacy while still providing recommendations with decent accuracy, we propose a randomized perturbation-based scheme.

Original languageEnglish (US)
Number of pages5
StatePublished - 2005
Event20th Annual ACM Symposium on Applied Computing - Santa Fe, NM, United States
Duration: Mar 13 2005Mar 17 2005


Other20th Annual ACM Symposium on Applied Computing
Country/TerritoryUnited States
CitySanta Fe, NM


  • Collaborative filtering
  • Privacy
  • Randomization
  • SVD

ASJC Scopus subject areas

  • Software


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