
ARPIT BHARDWAJ · Zenodo (CERN European Organization for Nuclear Research) 2026 · 2026
DOI: 10.5281/zenodo.23792966
Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).
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.
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