Personalized recommendation is to recommend information and goods that users are interested in according to their interest characteristics and purchase behavior. With the continuous expansion of the scale of e-commerce and the rapid growth of the number and types of goods, it takes customers a lot of time to find the goods they want to buy. This process of browsing a lot of irrelevant information and products will undoubtedly lead to the continuous loss of consumers submerged in information overload. In order to solve these problems, personalized recommendation system came into being.

Personalized recommendation system is an advanced business intelligence platform based on massive data mining, which helps e-commerce websites to provide completely personalized decision support and information services for their customers. The recommendation system of shopping websites recommends goods for customers, and automatically completes the personalized selection process of goods to meet the personalized needs of customers. The basis of recommendation is: the most popular products on the website, the city where the customer is located, the customer's past purchase behavior and purchase records, and speculate on the customer's possible future purchase behavior.

In the era of e-commerce, merchants provide a large number of goods through shopping websites, and customers can't know all the goods at once through the screen, nor can they directly check the quality of the goods. Therefore, customers need an electronic shopping assistant who can recommend products that customers may be interested in or satisfied with according to their hobbies.