Chatbot - beauty skin care products recommendations - Liew, Yi Kei (2021)
Skincare products work differently depending on the type of skin. Therefore, the project proposes a context-aware chatbot for skincare product recommendations based on skin types. Firstly, we collect genuine product reviews dataset using a custom web crawler on cosmetic websites. The dataset is preprocessed to remove noises like null value, incomplete reviews, and unverified reviews. Then, we built a sentiment analyzer based on DistilBERT to rate beauty products based on the positive and negative scores from the products reviews. Next, we train a skin type model to detect four skin types: dry, oily, combination and natural using a CNN. Then, we trained a recommendation system using a factorization machine to automatically recommend skincare products to users based on the skin types. Lastly, we built a chatbot in Telegram for users to input their facial image for skin detection and product recommendations.