Abstract
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 language | English |
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Article number | a16 |
Journal | ACM Transactions on Spatial Algorithms and Systems |
Volume | 5 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2019 Aug |
Externally published | Yes |
Keywords
- GPS trajectory
- Integer linear programming
- Point of interest
- Spatial-Temporal mining
ASJC Scopus subject areas
- Signal Processing
- Information Systems
- Modelling and Simulation
- Computer Science Applications
- Geometry and Topology
- Discrete Mathematics and Combinatorics