Facial Skincare Product Recommendation System for People with Facial Skin Problems
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Abstract
The objective of this study is to develop a product recommendation framework for individuals facial skin problems. The system's architecture consisted of four integral components: 1) collecting data on 5,143 skincare products, 2) provide recommendations for facial skin care products using the principle of Content Based Filtering. 3) product information retrieval using information retrieval principles, and 4) an intuitive user interface. The empirical study showed a commendable efficacy in product recommendation, with an average accuracy of 0.876 and an F-Measure value of 0.686 in assessing users' skin conditions. Similarly, the product search mechanism demonstrates notable precision, averaging 0.720 in precision and F-Measure of 0.636, indicative of satisfactory system usability and user interface engagement. The computed average satisfaction ratings of 4.397 and 4.399, respectively, corroborate the favorable user satisfaction levels. This research not only contributes to the practical domain of skincare but also to the academic research on personalized recommendation systems and information retrieval methodologies.
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