Enhancing E-Commerce Experience Through Intelligent Recommendation System

With the rise of smartphones, online shopping has grown quickly. Researchers have proposed applying several methodologies such as rules of association, collaborative filtering, Markov chains, and recurrent neural network techniques to offer optimal products to customers during their shopping experience. In this paper, the authors begin by explaining the general layout of an e-commerce system for recommendations. They also provide an overview of the key features functional modules and system architecture of the e-commerce recommendation system. The paper then discusses the e-commerce system's recommendation mechanism. Additionally, it enhances the system by utilizing convolutional in nature neural networks a kind of advanced AI, to generate better recommendations. Finally, using data from Alibaba, this paper examines and contrasts the performance of three widely used recommendation systems. The outcomes of the experiment show that the suggested recommendation method, which is based on deep learning and uses a convolutional neural network, surpasses the other two algorithms. It is also broadly applicable and has strong practical value. The use of e-commerce is increasing as smart computing and the internet grow. The goods that are offered on e-commerce platforms should be taken considered since they have the power to influence a user's purchasing choice. The aim of this paper is to develop a structure for e-commerce websites that makes product recommendations based on customer needs. This study uses the Slope One algorithm, where the product's domain ontology is used to determine the input rating. Product relationships are represented using domain ontology.