A Systematic Study on Recommendation System for E-Commerce Applications

This E-commerce is a widespread industry across the world that has revolutionized the way businesses operate and consumers engage in transactions. However, despite its rapid growth and technological advancements, e-commerce platforms face significant challenges that hinder their efficiency and effectiveness. One of them is recommending the right product to the user. The paper makes use of neural networks, matrix factorization and reinforcement learning to develop a highly accurate recommendation system that produces optimized results and enhances the user experience. Further, this paper compares and analyses the performance of some commonly used recommendation systems on Amazon data sets. Experimental results show that the recommendation system proposed by the combination of neural networks and matrix factorization is much better than the other algorithms. The basic purpose of a search engine is to simply convert the user search statements into keywords which are then sent to the server to produce the desired result. An accurate recommendation system offers a dual benefit: it not only saves time but also boosts the likelihood of customers making purchases.