<![CDATA[The growing popularity of diecast car collections has created a demand for efficient recommendation systems to assist collectors in discovering new products. This study focuses on the development of a content-based filtering (CBF) recommendation system for diecast car products. The system employs the TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity techniques to calculate the relevance between products and user preferences. By analyzing the textual features of diecast car products, such as brand, model, and specifications, the CBF system generates personalized recommendations based on similarity scores. The evaluation of the system's performance demonstrates its effectiveness in providing accurate and relevant recommendations, which enhance the user experience and facilitate the exploration of the diecast car market. ]]>
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