A deep learning based predictive model to recommend a list of products based on users’ previous reviews, the product the user is currently viewing and the initial search query that he provided.

• Build an efficient Product Recommender System.
• Recommendations are based on Collaborative Filtering, Deep Learning, Transfer Learning and Clustering.
• The system must suggest products to the user which might be on interest based on his past reviews and past products viewed.

This project aims to build a system to recommend products to a user in three ways:

• Allow the user to click images of the products he liked and recommend visually similar products.
• If a user wants to look at a particular type of product like “blue jeans”, the system must recommend products similar to blue jeans.
• If a user is currently viewing a product, the system must recommend products similar to the product he is currently viewing.
• If there is a new user who hasn’t reviewed any products, the system should recommend something to him.
• The system must make the predictions fast in a low time complexity. This is an effort to implement mathematical predictive models to improve user experience by making them personalised/relevant recommendations. We believe in enhanced user experience by taking feedback/rating from him.