AI/ML-Smart Tourist Recommendation System

This paper presented an AI/ML-based smart tourist recom-mendation system that combines a web application with a machine learning recommendation component. The proposed architecture uses React/Vite, FastAPI, SQLite, and Python-based recommendation processing. The current recommendation baseline uses TF-IDF feature extraction and cosine similarity to match user preferences with destination information. Additional factors such as budget, travel pace, and accessibility can be incorporated into the ranking process. The modular architecture provides a foundation for fu-ture development including collaborative filtering, hybrid recommendation, semantic embeddings, contextual information, itinerary optimization, and systematic evaluation. The current prototype demonstrates functional integration, while larger verified datasets and controlled experiments are required for quantitative validation.

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