# Constraint-based recommender system for crisis management simulations

arXiv: https://arxiv.org/abs/2306.04553 · HTML: https://mlsanity.com/paper/2306.04553/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2023.

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

- [Development of Ontological Knowledge Bases by Leveraging Large Language Models](https://mlsanity.com/paper/2601.10436/) (2026)
- [Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning](https://mlsanity.com/paper/2504.06274/) (2025)

## Abstract (arXiv)

In the context of the evacuation of populations, some citizens/volunteers may want and be able to participate in the evacuation of populations in difficulty by coming to lend a hand to emergency/evacuation vehicles with their own vehicles. One way of framing these impulses of solidarity would be to be able to list in real-time the citizens/volunteers available with their vehicles (land, sea, air, etc.), to be able to geolocate them according to the risk areas to be evacuated, and adding them to the evacuation/rescue vehicles. Because it is difficult to propose an effective real-time operational system on the field in a real crisis situation, in this work, we propose to add a module for recommending driver/vehicle pairs (with their specificities) to a system of crisis management simulation. To do that, we chose to model and develop an ontology-supported constraint-based recommender system for crisis management simulations.