1) Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback.
2) Recommender systems are active information filtering systems which personalize the information coming to a user based on his interests, relevance of the information etc. Recommender systems are used widely for recommending movies, articles, restaurants, places to visit, items to buy etc.
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's tought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. In this Project, we are going to create a content-based recommendation system where the 'content' will be the chemical components of cosmetics.