Handling the data growth with privacy preservation in collaborative filtering

Xiwei Wang, Jun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


The emergence of electric business facilitates people in purchasing merchandises over the Internet. To sell the products better, online service providers use recommender systems to provide recommendations to customers. Most recommender systems are based on collaborative filtering (CF) technique. This technique provides recommendations based on users' transaction history. Due to the technical limitations, many online merchants ask a third party to help develop and maintain recommender systems instead of doing that themselves. Therefore, they need to share their data with these third parties and users' private information is prone to leaking. Furthermore, the fast data growth should be handled by the data owner efficiently without sacrificing privacy. In this chapter, we propose a privacy preserving data updating scheme for collaborative filtering purpose and study its performance on two different datasets. The experimental results show that the proposed scheme does not degrade recommendation accuracy and can preserve a satisfactory level of privacy while updating the data efficiently.

Original languageEnglish
Title of host publicationIAENG Transactions on Engineering Technologies - Special Volume of the World Congress on Engineering 2012
Number of pages13
StatePublished - 2013
Event2012 World Congress on Engineering, WCE 2012 - London, United Kingdom
Duration: Jul 4 2012Jul 6 2012

Publication series

NameLecture Notes in Electrical Engineering
Volume229 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference2012 World Congress on Engineering, WCE 2012
Country/TerritoryUnited Kingdom


  • Collaborative filtering
  • Data growth
  • Missing value imputation
  • Non-negative matrix factorization
  • Privacy preservation
  • Singular value decomposition
  • Updating

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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