A Content-based Skincare Product Recommendation System

Consumer interest in cosmetics, and particularly skincare products, has surged globally in recent years. Traditional methods of selecting skincare products involve relying on best-sellers or in-store recommendations. However, these approaches are ineffective because they fail to account for individual variations in skin conditions and consumer compatibility. This research aims to design a skincare product recommendation system based on users' skin types and ingredient compositions of products. The proposed method employs content-based filtering to identify chemical components of products and find products with similar ingredient compositions. Unlike many existing sys-tems that require users to input product names, the new system takes into account their desired beauty effects to accommodate those with limited skincare knowledge. The resulting system returns personalized recommendations across multiple product categories. It could contribute to improved compatibility between users and skincare products, enhanced user satisfaction and streamlined selection processes.