User Interest Discovery and Prediction Service Model in E-commerce Recommendation

With the popularity of Internet and the rapid development of e-commerce, the problem of massive information overload is highlighted. To effectively solve such contradiction, this paper introduces the related concepts of data mining and e-commerce recommendation system, and expounds the operation process of BP neural network model. Then, it studies the personalized recommendation algorithm integrating user preference model and BP neural network, and discusses the problem of establishing user preference model and training the neural network optimized by genetic algorithm(GA). The preference model of the target user can not only fully realize the global search function of GA, but also combine the prediction ability of BP neural network to make the algorithm more accurate for prediction and recommendation. The experiment proves that the application of such method to e-commerce recommendation system can also achieve better recommendation effect.

Automated summary

This paper introduces how to use data mining and e-commerce recommendation system to solve the problem of information overload, proposes a personalized recommendation algorithm integrating user preference model and BP neural network, and proves that applying this method to e-commerce recommendation system can achieve better recommendation effect.