Construction of E-Commerce User Profile Design Model Based on Deep Learning Algorithm

Abstract

In rural e-commerce platforms, user behaviors are complex and diverse, and it is difficult for the recommendation system to accurately predict demand, resulting in a low conversion rate of the platform. Based on deep learning, this paper uses deep learning models, that is, models, to analyze user browsing, purchasing, and feedback behaviors, and optimize recommendation algorithms. The research process mainly includes user portrait design and model construction, model description recommendation and optimization, system integration, and application. According to the experiment, the recommendation accuracy of the rural e-commerce user portrait recommendation system increased by 20% or more, and the purchase conversion rate increased by 44.23%. It can be seen that deep learning, especially the recommendation system, can significantly improve the user experience and sales performance of rural e-commerce platforms, especially in personalized recommendation and real-time feedback optimization. On the whole, deep learning can show good performance and high application value in the rural e-commerce recommended user portrait.