HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection

Mouna Rekik, Mouna Rekik, Rima Grati, Ichrak Benmohamed, Khouloud Boukadi

2024

Abstract

The pervasive influence of smartphones and mobile internet has revolutionized journey planning, particularly transportation. With navigation applications delivering real-time information, recommender systems have emerged as crucial tools for enhancing the travel experience. This paper introduces HybridCRS-TMS, a unique Hybrid Collaborative Recommender System for Transport Mode Selection, leveraging a dataset of 260 passengers. Through advanced data mining techniques, specifically k-Nearest Neighbors (k-NN) for collaborative recommendations and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis for objective evaluation, the system provides personalized transportation mode recommendations. The model not only demonstrates exceptional performance but also showcases the synergy between collaborative and objective decision-making approaches, contributing to efficient, personalized, and well-informed travel solutions. This study underscores the system’s versatility, illustrating its ability to optimize travel choices through a hybrid recommendation framework that integrates both collaborative and objective criteria.

Paper Citation

in Harvard Style

Rekik M., Grati R., Benmohamed I. and Boukadi K. (2024). HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection. In Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-706-1, SciTePress, pages 383-394. DOI: 10.5220/0012758300003753

in Bibtex Style

@conference{icsoft24,
author={Mouna Rekik and Rima Grati and Ichrak Benmohamed and Khouloud Boukadi},
title={HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection},
booktitle={Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2024},
pages={383-394},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012758300003753},
isbn={978-989-758-706-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Software Technologies - Volume 1: ICSOFT
TI - HybridCRS-TMS: Integrating Collaborative Recommender System and TOPSIS for Optimal Transport Mode Selection
SN - 978-989-758-706-1
AU - Rekik M.
AU - Grati R.
AU - Benmohamed I.
AU - Boukadi K.
PY - 2024
SP - 383
EP - 394
DO - 10.5220/0012758300003753
PB - SciTePress