Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph

Mozhgan Taheri, M. Farnaghi*, Abbas Alimohammadi, Parham Moradi, Samira Khoshahval

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Downloads (Pure)

Abstract

Previous studies neglected the impact of shared spatio-temporal interests in the Point of Interest (POI) recommendation. The proposed GHTG-ERWR method simultaneously models shared spatio-temporal preferences of users, the dynamics of users’ behavior, and the geographical effect for POI recommendation. It is composed of an Extended Random Walk with Restart (ERWR) algorithm that works on a Geographical-temporal Hybrid Tripartite Graph (GHTG). The method overcomes shortcomings of prior approaches by employing users’ joint preferences over time and space through the auxiliary information derived from location-session and session-location edges. Experimental results on Gowalla and Weeplaces datasets proved the feasibility of the approach.
Original languageEnglish
Pages (from-to)1-19
Number of pages19
JournalJournal of spatial science
Early online date22 Mar 2021
DOIs
Publication statusPublished - 22 Mar 2021

Keywords

  • Location-based social networks
  • graph-based recommendation
  • Spatio-temporal
  • graph
  • Random walk
  • common spatio-temporal preferences
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D

Fingerprint Dive into the research topics of 'Point-of-interest recommendation using extended random walk with restart on geographical-temporal hybrid tripartite graph'. Together they form a unique fingerprint.

Cite this