Personalized Skincare Product Recommendation System Using Content-Based Machine Learning

Beyond the limitations of conventional methods, the growing demand for skincare products has created a need for more individualized recommendations. This paper presents a novel method for creating a machine learning-based skincare product recommendation system using content-based filtering. The system provides customized recommendations by examining the chemical makeup of products and taking individual skin types into account. By entering their preferred beauty impacts, users can improve the system’s ability to adjust to their preferences. The implementation of user interface design, product composition assessment methodology, and content-based filtering are described. The system’s ability to provide tailored recommendations is demonstrated by empirical evidence, and it holds great promise for transforming consumer experiences in the cosmetics industry.