Research Article
RAWAH: An AI-based Tourism Recommendation System using Embedding Models
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 187 - Issue 115 |
| Published: June 2026 |
| Authors: Ghala Aldeheem, Lamees Aldaej, Roqayah Bahubail, Retaj Alahmari, Ghala Alfawzan |
10.5120/ijca1fb271c1eb92
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Ghala Aldeheem, Lamees Aldaej, Roqayah Bahubail, Retaj Alahmari, Ghala Alfawzan . RAWAH: An AI-based Tourism Recommendation System using Embedding Models. International Journal of Computer Applications. 187, 115 (June 2026), 50-54. DOI=10.5120/ijca1fb271c1eb92
@article{ 10.5120/ijca1fb271c1eb92,
author = { Ghala Aldeheem,Lamees Aldaej,Roqayah Bahubail,Retaj Alahmari,Ghala Alfawzan },
title = { RAWAH: An AI-based Tourism Recommendation System using Embedding Models },
journal = { International Journal of Computer Applications },
year = { 2026 },
volume = { 187 },
number = { 115 },
pages = { 50-54 },
doi = { 10.5120/ijca1fb271c1eb92 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2026
%A Ghala Aldeheem
%A Lamees Aldaej
%A Roqayah Bahubail
%A Retaj Alahmari
%A Ghala Alfawzan
%T RAWAH: An AI-based Tourism Recommendation System using Embedding Models%T
%J International Journal of Computer Applications
%V 187
%N 115
%P 50-54
%R 10.5120/ijca1fb271c1eb92
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents RAWAH, an AI-based mobile application which provides personalized tourism recommendations for Saudi Arabia. The system uses advanced embedding models to match individual preferences with appropriate travel destinations which helps to enhance user experience. The four embedding models tested in this study included OpenAI text-embedding-3-small and Sentence-BERT and BGE (bge-small-en-v1.5). The models were tested under the same conditions using real user preference data and place descriptions and their performance was measured using Precision@5 and Recall@5 and F1-score metrics. The results show that the SBERT (all-mpnet-base-v2) model achieved the best overall performance which showed a higher ability to capture semantic similarity and create precise recommendations. The selected model was integrated into the RAWAH application to improve its recommendation system. The proposed system creates intelligent tourism solutions through its combination of artificial intelligence and real-time data and user-centered design.
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