Research on e-commerce recommendation system based on matrix factorization algorithm

The development of the internet has brought great convenience to people's travel and shopping. More and more people choose to shop online. As e-commerce continues to grow in scale, the number and variety of products are also growing rapidly, which results in customers taking a lot of time to find the products they want to buy. This problem prevents people from using the Internet quickly and efficiently. In order to solve these problems, personalized recommendation system comes into being. It can directly predict the content that users may be interested in based on their historical behavior, and make personalized recommendations for them in the massive data. Based on the idea of collaborative filtering, this paper adopts matrix factorization method to analyze the sales records of an e-commerce platform, and analyzes the potential preferences of 686 customers, and gives the top five personalized recommended products StockCode of users.