Mobile Application Based on Clean Architecture for Sustainable Tourism Recommendation Using BERT

Abstract

The development of intelligent systems requires robust architectures and design patterns that ensure scalability, maintainability, and separation of concerns. This paper presents the design and implementation of a mobile application for recommending sustainable tourist destinations in Peru, employing a Clean Architecture approach in the backend and the MVC pattern in the frontend. The solution integrates a content-based recommendation engine using semantic embeddings generated through BERT, connected to open data sources from MINCETUR, SafeTravels, and OpenStreetMap. The proposed system was evaluated using three simulated user profiles, achieving an average similarity score of 0.83 and a more balanced distribution between traditional and emerging destinations. From a software engineering perspective, the system demonstrates decoupling between layers, modularity, interoperability with external APIs, and scalability potential. This architecture serves as a practical case study of applying modern design patterns to intelligent systems with an impact on sustainable tourism.