Optimizing Tourism Recommendations with a Hybrid Model: Bridging User Preferences and Behavioral Patterns

Optimizing Tourism Recommendations with a Hybrid Model: Bridging User Preferences and Behavioral Patterns

apriori; content-based filtering; cosine similarity; hybrid model; tourism

Recommender systems play a crucial role in personalized decision-making, particularly in the tourism industry, where users seek destinations that align with their preferences. However, traditional recommendation methods often struggle to provide accurate recommendations. This researchintroduces a hybrid recommendation framework that combines Content-Based Filtering (CBF) with Apriori-based association rule mining to improve the accuracy and relevance of recommendations. First, CBF was implemented using TF-IDF, Word2Vec, and BERT embeddings to compute the similarity between user preferences and tourism destinations. The Top-N recommended destinations from each method were then used as antecedents in Apriori to identify associative patterns and co-occurrence relationships among tourism destinations. By leveraging both semantic preference matching and association rule mining, the proposed system refines the recommendation process, ensuring not only personalized suggestions but also uncovering implicit travel patterns. The experimental results demonstrate that the hybrid model improves recommendation relevance and accuracy compared to standalone CBF methods. The accuracy of the CBF model was 53.96%, whereas that of the hybrid model was 94.31%. The integration of CBF and Apriori offers a morecomprehensive and data-driven recommendation framework, which is valuable for personalized tourism applications

Rifqi Hammad1*, Muhammad Azwar2, M. Aswin Syarif

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6510/1122

Department of Software Engineering, University of Bumigora, Mataram, Indonesia

August 22, 2025

FAJAR BAGUS W

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