Personalized Destination Forecasting and Recommendation: A Multi-Phase AI-Driven Approach for the Tourism Industry

DOI

10.2991/978-94-6463-716-8_43How to use a DOI?

Keywords

Destination Forecasting; Recommendation system; Tourism; VGGNet; SqueezeNet

Abstract

Destination Forecasting and Recommendation encompasses the projection of user-favored travel locales predicated on historical data, individual preferences, and prevailing trends, succeeded by the provision of customized recommendations aimed at optimizing travel arrangements. While current methodologies are hindered by issues related to scalability, computational inefficiency, and a paucity of personalization, they concurrently exhibit deficiencies in reducing inaccuracies such as false positives and false negatives, thereby compromising the reliability of recommendations. To address these challenges, this research introduces a multi-phase AI-driven framework for personalized destination forecasting and recommendation specifically within the tourism sector. The proposed framework initiates with sophisticated data preprocessing methodologies, which include tokenization, stemming, stopword elimination, and TF-IDF, to enhance the quality of the data. Feature extraction is executed via Part-of-Speech (POS) tagging, while the predictive modeling phase amalgamates the computational efficiency of SqueezeNet with the superior feature extraction capabilities of VGGNet through the innovative hybrid architecture known as VGGFire Net. Recommendations are formulated utilizing collaborative filtering techniques alongside Jaccard similarity to accurately discern user preferences. The proposed model achieved outstanding results on Dataset, with 70% training and 30% testing split, the model got 98.55% accuracy, 96.88% precision, 97.78% sensitivity, and 98.44% specificity. For Dataset, with an 80% training and 20% testing split, the model also demonstrated low FPR: 0.0821, FNR: 0.0810. Implemented in Python, this methodology proved its reliability, scalability, and potential to revolutionize personalized travel experiences in the dynamic tourism industry.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Abhishek Tiwari
AU  - Pratosh Bansal
PY  - 2025
DA  - 2025/05/26
TI  - Personalized Destination Forecasting and Recommendation: A Multi-Phase AI-Driven Approach for the Tourism Industry
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 552
EP  - 569
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_43
DO  - 10.2991/978-94-6463-716-8_43
ID  - Tiwari2025
ER  -