Recommendation algorithm for mobile E-commerce based on cone depth learning

Aiming at the real user - commodity behavior data of Alibaba mobile e-commerce platform, the big data development of mobile e-commerce is analyzed in this paper, and a recommendation algorithm based on the mobile e-commerce deep learning is put forward. The study of the recommendation algorithms applies to the information age with the rapid growth of data. The results have verified that the recommendation algorithm proposed in this paper can extract useful information features from extensive information and recommend to the users who need such information to meet the information needs of different users, save the time required for users to search for the information, and improve the utilization of the information. These results indicate that the mobile e-commerce recommendation algorithm based on the cone depth learning has good practicality. It is concluded in this paper that the recommendation algorithm has a relatively high recommendation effect in the mobile e-commerce scenario and that its ideas and results have essential value for the study of mobile e-commerce.