Personalized visited-POI assignment to individual raw GPS trajectories

Jun Suzuki, Yoshihiko Suhara, Hiroyuki Toda, Kyosuke Nishida

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Knowledge discovery from GPS trajectory data is an essential topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This article proposes a task that assigns personalized visited points of interest (POIs). Its goal is to assign every fine-grain location (i.e., POIs) that a user actually visited, which we call visited-POI, to the corresponding span of his or her (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as span candidates of visits using a variant of a conventional stay-point extraction method and then extract POIs that are located close to the extracted stay-points as visited-POI candidates. Then, we simultaneously predict which stay-points and POIs can be actual user visits by considering various aspects, which we formulate as integer linear programming. Experimental results conducted on a real user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.

Original languageEnglish
Article numbera16
JournalACM Transactions on Spatial Algorithms and Systems
Issue number3
Publication statusPublished - 2019 Aug


  • GPS trajectory
  • Integer linear programming
  • Point of interest
  • Spatial-Temporal mining


Dive into the research topics of 'Personalized visited-POI assignment to individual raw GPS trajectories'. Together they form a unique fingerprint.

Cite this