A personalized products selection assistance based on e-commerce machine learning

The prosperity of electronic commerce has changed the traditional trading behaviors. More and more people are willing to perform Internet shopping. At the same time, consumers experience information overload and look for help in selecting from an overwhelming array of products. In order to overcome such a problem one option is to develop a personalized online assistance to retrieve product information that really matters for the customers. In this paper, we present a method that combines the genetic algorithm and k nearest neighbor technology to reason about the customer's personal preferences from his/her profile and then provide the most appropriate products to meet his/her needs. Our experimental results are showing that our systems have a bright future.