Facial skin functions as a protective barrier against environmental pollution, including ultraviolet rays, which can cause wrinkles, aging, acne, and enlarged pores. Additionally, an unbalanced diet, lack of rest, and exposure to free radicals can further worsen skin conditions. Facial skincare is crucial as it relates to personal identity and health. Facial skin types are categorized into five groups: normal, dry, oily, combination, and sensitive, classified based on water and oil levels in the skin. A skincare product recommendation model is needed to assist consumers in finding products suitable for their skin issues. This need becomes increasingly significant given the wide variety of facial skincare products available in the market today. This study developed a recommendation model using the content-based filtering (CBF) method, which considers product characteristics such as ingredient composition. Experimental results show that the model effectively provides recommendations aligned with user preferences. The model demonstrated good performance, achieving an accuracy rate of 88.89%.