<![CDATA[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 study proposes a hybrid recommendation model that integrates Content-Based Filtering (CBF) and Apriori association rule mining to enhance recommendation quality. 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 more comprehensive and data-driven recommendation framework, which is valuable for personalized tourism applications.]]>
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