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

Document Type

Conference Proceeding

Source of Publication

Proceedings of the 19th International Conference on Software Technologies

Publication Date

1-1-2024

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 vers atility, illustrating its ability to optimize travel choices through a hybrid recommendation framework that integrates both collaborative and objective criteria.

ISBN

978-989-758-706-1

Publisher

SCITEPRESS - Science and Technology Publications

First Page

383

Last Page

394

Disciplines

Computer Sciences

Keywords

Transport mode, Recommender System, Optimal selection, Collaborative System, TOPSIS

Indexed in Scopus

no

Open Access

no

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