RecoTrip: An AI-Powered Mobile Travel Recommendation System with Machine Learning

This paper presents RecoTrip, an intelligent mobile travel recommendation system that leverages machine learning algorithms and data analytics to provide personalized travel suggestions. The system is built using Python for the backend with integrated machine learning models, and React Native (Expo) for the frontend mobile application. RecoTrip analyzes user preferences, historical travel patterns, budget constraints, and destination characteristics to generate customized itineraries and point-of-interest recommendations.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-30
DOI
https://doi.org/10.5281/zenodo.23792966
Primary Topic
Recommender Systems and Techniques
Type
preprint
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preprint

RecoTrip: An AI-Powered Mobile Travel Recommendation System with Machine Learning

ARPIT BHARDWAJ
3 links
Zenodo (CERN European Organization for Nuclear Research)
Recommender Systems and Techniques
preprint

RecoTrip: An AI-Powered Mobile Travel Recommendation System with Machine Learning

ARPIT BHARDWAJ
preprint en

Abstract

This paper presents RecoTrip, an intelligent mobile travel recommendation system that leverages machine learning algorithms and data analytics to provide personalized travel suggestions. The system is built using Python for the backend with integrated machine learning models, and React Native (Expo) for the frontend mobile application. RecoTrip analyzes user preferences, historical travel patterns, budget constraints, and destination characteristics to generate customized itineraries and point-of-interest recommendations.

Zenodo (CERN European Organization for Nuclear Research)
Industry, innovation and infrastructure
Recommender Systems and Techniques
DOIOpen Access PDFOpenAlex
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RecoTrip: An AI-Powered Mobile Travel Recommendation System with Machine Learning — ARPIT BHARDWAJ · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS