Successive Point-of-Interest Recommendation with Local Differential Privacy

Jong Seon Kim, Jong Wook Kim, Yon Dohn Chung

Research output: Contribution to journalArticlepeer-review

Abstract

A point-of-interest (POI) recommendation system performs an important role in location-based services because it can help people to explore new locations and promote advertisers to launch advertisements at appropriate locations. The existing POI recommendation systems require raw check-in history of users, which might cause location privacy violations. Although there have been several matrix factorization (MF) based privacy-preserving recommendation systems, they can only focus on user-POI relationships without considering the human movements in check-in history. To tackle this problem, we design a successive POI recommendation framework with local differential privacy, named SPIREL. SPIREL uses two types of information derived from the check-in history as input for the factorization: a transition pattern between two POIs and the visit counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further integrate local differential privacy mechanisms in our proposed framework to prevent potential location privacy breaches. Experiments using four public datasets demonstrate that SPIREL achieves better POI recommendation quality while accomplishing stronger privacy preservation.

Original languageEnglish
Article number9420065
Pages (from-to)66371-66386
Number of pages16
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Point-of-Interest
  • local differential privacy
  • matrix factorization
  • recommendation system

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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