Design of a computer-based personalized recommendation service system for e-commerce

Given that e-commerce gives customers more alternatives, its structure gets more and more complicated, resulting in information overload. The solution to this problem is an e-commerce personalized recommendation system that makes use of machine learning technology. This study looks at e-commerce’s usage of personalized recommendation technologies. It accomplishes this by carefully examining the algorithms and other technologies employed in the e-commerce recommendation system, and then, considering the systems present development stage, recommending the most recent design. The offline mining and online recommendation parts comprise the two divisions of the system. After that, it assesses and applies each section’s characteristics and technologies, suggesting correctness and meeting urgent demands. This paper offers a helpful illustration of a model based on the factors influencing the retail industry’s customized e-commerce information suggestions. It accomplishes this by outlining the relevant theories, characteristics, and widely used machine learning-based customized recommendation solutions. The results show that a customer’s income level, online shopping experience, commodity pricing, product quality, suggestion relevancy, credit evaluation, and service quality will all have a substantial beneficial influence on their propensity to purchase. In the end, these elements will affect how the buyer behaves while making purchases. E-commerce platforms could leverage this influencing factor to provide a customized information recommendation service.