Shopping Recommendation System Design Based On Deep Learning

Abstract

With the development of internet shopping, the amount of user data generated is increasing day by day. In this paper, a shopping recommendation system based on deep learning is constructed. The user data crawling module and shopping recommendation module are mainly designed. Firstly, obtain important user review information and product information from Jingdong Mall by python crawler and build a user data crawling module. Then a shopping recommendation system was constructed based on deep learning, combined with recommendation algorithm. The system extracts the characteristics of users and commodities through neural network algorithms, proposing a coupled recommendation algorithm (referred to as U-S recommendation algorithm) based on user characteristics and product similarity. The algorithm calculates the best match rate between users and commodities. The results show that the proposed algorithm can improve the effectiveness of the recommendation system, compared with the algorithm based on similarity of products.