Personalized Product Recommendation System for Amazon

Amazon's Personalised Product Recommendation System greatly enhances the shopping experience on the internet by providing product recommendations to users based on the type of items they have chosen to purchase on previous browsing histories and other actions that they have taken in the past. By using machine learning based techniques the system is able to accurately forecast products that will match the preferences of individual users which then leads to the improving customer satisfaction result together with an enhance conversion of sales figures from sales given over previous periods. Currently, in the current system, Amazon's recommendation engine mostly uses classical method, popularity-based filtering and collaborative filtering. These techniques, however, usually offer general advice which does not fully consider individual user preferences, resulting in lower engagement and unproductive product discovery. In the proposed system, a combination of leading recommendation algorithms (namely, Collaborative Filtering, Content-Based Filtering, Hybrid Models) is used to yield better quality and personalized suggestions. Various methods, including TF-IDF for text-based recommendation, Cosine Similarity for user-product relation, and Matrix Factorization (SVD) for latent feature extraction, are used to improve the quality of the recommendation. This paradigm guarantees an adaptive and personalised shopping experience that can be optimised both in terms of engagement of the user and revenue.