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All Journal International Journal of Advances in Applied Sciences
Trung, Nguyen Quang
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Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A method classifying the domestic tourist destination base similarity measuring Hoi, Nguyen Thi; Nhung, Tran Thi; Truong, Bui Quang; Trung, Nguyen Quang
International Journal of Advances in Applied Sciences Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i3.pp740-750

Abstract

The classification problem is crucial in business, providing an effective method for supporting search activities in areas such as e-commerce, education, and marketing. This has become especially important in the wake of the COVID-19 pandemic, which has increased the need to promote and stimulate domestic tourism. This research focuses on recommending tourist destinations based on historical search data related to domestic tourism. The study uses techniques like term frequency-inverse document frequency (TF-IDF) weight vector analysis and similarity measures to calculate recommendation scores. Data was collected from various tourism websites, covering destinations across all 63 provinces and cities in Vietnam. Experiments were conducted using three approaches: cosine similarity, the brute force algorithm, and long short-term memory (LSTM) for long-text processing. The results indicate that similarity-based methods produce recommendations that closely match user preferences. For full-sentence queries, the brute force algorithm delivers more accurate results, while LSTM provides faster processing times. These findings offer businesses multiple strategies for improving recommender systems in practical applications.
Co-Authors Hoi, Nguyen Thi Nhung, Tran Thi Truong, Bui Quang