Privacy-preserving SOM-based recommendations on horizontally distributed data

KALELİ C., Polat H.

KNOWLEDGE-BASED SYSTEMS, vol.33, pp.124-135, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33
  • Publication Date: 2012
  • Doi Number: 10.1016/j.knosys.2012.02.013
  • Journal Name: KNOWLEDGE-BASED SYSTEMS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.124-135
  • Keywords: Privacy, Distributed data, Clustering, Recommendation, Performance
  • Anadolu University Affiliated: Yes

Abstract

To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the same products can be distributed among various companies, even competing vendors. Therefore, those companies holding inadequate number of users' data might decide to combine their data in such a way to present accurate predictions with acceptable online performance. However, they do not want to divulge their data, because such data are considered confidential and valuable. Furthermore, it is not legal disclosing users' preferences: nevertheless, if privacy is protected, they can collaborate to produce correct predictions.