Recommendation systems will help businesses improve their customer's experience and result in better customer acquisition and retention.
The recommendation system, we are creating in this project has three sections:
1. A recommender for a new customer who lands on the business’s website for the first time, this recommendor is a product popularity based system called content-based-filtering
2. A recommender for a returning customer based on customer's purchase history and ratings provided by other users who bought similar items called collaborative-filtering
3. A recommender for a business website when setting up its e-commerce for the first time without any product rating using a hybrid recommender system which is a combination of previous recommenders
When a new customer without any previous purchase history visits the e-commerce website for the first time, they are recommended the most popular products sold on the company's website using a content based filtering system. Once, they make a purchase, the recommendation system updates and recommends other products based on the purchase history and ratings provided by other users on the website using collaborative filtering techniques.