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Machine Learning-Based Tourism Site Recommendation System: A Case Study in Loja

In: Management, Tourism, and Smart Technologies, Vol 2

Author

Listed:
  • Luis Sánchez

    (Universidad Técnica Particular de Loja, Computer Science and Electronic Department)

  • Priscila Valdiviezo-Diaz

    (Universidad Técnica Particular de Loja, Computer Science and Electronic Department)

  • Clara Gonzaga-Vallejo

    (Universidad Técnica Particular de Loja, Business Sciences Department)

Abstract

Loja city in Ecuador boasts a wide range of tourist attractions, and it can be overwhelming for visitors to decide where to go due to the numerous options available. To address this issue, this paper employs a collaborative filtering-based recommendation model to provide users with personalized suggestions based on their past ratings. We tested two machine learning models: KNN and SVD, as well as the Autoencoder deep learning model, on the Loja City tourism sites dataset. These algorithms were evaluated using standard metrics such as RMSE, Precision, and Recall. The results show that SVD outperforms both KNN and the Autoencoder deep learning model. This finding confirms the effectiveness of SVD in capturing latent factors in sparse data, thereby improving the quality of tourism recommendations.

Suggested Citation

  • Luis Sánchez & Priscila Valdiviezo-Diaz & Clara Gonzaga-Vallejo, 2026. "Machine Learning-Based Tourism Site Recommendation System: A Case Study in Loja," Springer Proceedings in Business and Economics, in: Pedro Miguel Gaspar & José Machado & João Paulo Ramos Teixeira & José Avelino Moreira Victor & Carlo (ed.), Management, Tourism, and Smart Technologies, Vol 2, chapter 16, pages 201-210, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-24600-4_16
    DOI: 10.1007/978-3-032-24600-4_16
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