Deep learning-based NLP model to design text-based product recommendation system for dermatological products
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Abstract
Human skin is a complex and highly variable organ influenced by multiple factors such as skin tone, hair density, lifestyle, and environmental exposure. Dermatological disorders are one of the most widespread disorders of the world and are increasing because of many environmental, geographical factor variations. Several skin diseases are infectious in nature and require timely intervention to prevent progression and long-term complications. The shortage of dermatologists and general practitioners in developing countries makes things worse and hampers the service delivery. As a result, individuals often rely on informal recommendations or home remedies, which may worsen existing conditions. Artificial Intelligence (AI) has the potential to bridge this gap by improving access to reliable skincare knowledge and personalized product recommendations. It will enhance collaboration among physicians and cosmetic suppliers, further encouraging innovation and contribute to the better evaluation of cosmetic ingredients concerning safety and efficacy.AI applications are vast in healthcare; still, dermatology remains far behind. In this work, a text-based skincare product recommendation system is proposed using Natural Language Processing techniques. The study explores multiple NLP approaches and adopts TF-IDF for feature extraction due to its computational efficiency and suitability for unsupervised recommendation scenarios. Since TF-IDF-based systems cannot be evaluated using conventional accuracy metrics, manual validation was performed on a curated test set. Experimental results demonstrate that the proposed approach effectively recommends appropriate dermatological and natural skincare treatments based on user preferences and skin concerns
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