# Personalized Visited-POI Assignment to Individual Raw GPS Trajectories

arXiv: https://arxiv.org/abs/1901.06257 · HTML: https://mlsanity.com/paper/1901.06257/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 2 of 2)

- [DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision](https://mlsanity.com/paper/2306.06068/) (2023)
- [POIFormer: A Transformer-Based Framework for Accurate and Scalable Point-of-Interest Attribution](https://mlsanity.com/paper/2507.09137/) (2025)

## Abstract (arXiv)

Knowledge discovery from GPS trajectory data is an important topic in several scientific areas, including data mining, human behavior analysis, and user modeling. This paper proposes a task that assigns personalized visited-POIs. Its goal is to estimate fine-grained and pre-defined locations (i.e., points of interest (POI)) that are actually visited by users and assign visited-location information to the corresponding span of their (personal) GPS trajectories. We also introduce a novel algorithm to solve this assignment task. First, we exhaustively extract stay-points as candidates for significant locations using a variant of a conventional stay-point extraction method. Then we select significant locations and simultaneously assign visited-POIs to them by considering various aspects, which we formulate in integer linear programming. Experimental results conducted on an actual user dataset show that our method achieves higher accuracy in the visited-POI assignment task than the various cascaded procedures of conventional methods.