The application of factorization machines in user behavior prediction
Yuqi Wang, Wenqian Shang, Zhenzhong Li · 2016
With the development of Internet, shopping on the internet is becoming more and more popular. In the meantime, the massive commodity make the experience of online shopping bad. In order to solve this problem, commodity recommendation system is applied into e-commerce platform and the experience of online shopping has been promoted. Essentially, commodity recommendation is a kind of behavior prediction. So, research on user behavior prediction can also promote experience of online shopping. In this paper, the FMs algorithm is applied into prediction of user behavior. Based on real data of user behavior data, we take advantage of four kinds of behavior to make analysis and propose a good user behavior prediction model based on FMs algorithm.