Abstract:
"The research initiative aims to streamline cosmetics selection by developing a personalized cosmetics-selling website. This platform uses a recommendation engine that employs content-based and collaborative filtering, powered by the cosine similarity algorithm. It analyzes users' browsing histories to identify products of interest, making tailored product recommendations for each customer.
To assess the system's effectiveness, data science metrics like accuracy, recall, and F1-score were used. The results demonstrated the recommendation engine's high precision and recall, confirming its success in suggesting suitable cosmetic products based on user browsing history. Additionally, usability testing affirmed the website's user-friendly and intuitive design.
In summary, this research presents a practical solution for cosmetics selection by offering a user-friendly website that leverages advanced algorithms to provide personalized product recommendations. This enhances the shopping experience and product satisfaction for customers."
