Deep Learning-based Intelligent Marketing System Application Analysis

With the development of artificial intelligence and big data technology, an intelligent marketing system that responds to customers' shopping needs by analyzing user behavior data has emerged. The system can formulate more accurate and personalized marketing strategies, thus significantly improving customers' shopping efficiency and satisfaction. This paper focuses on the core technical architecture of the deep learning-based intelligent marketing system, including the information collection system, the data algorithm system, the dialog system and its related technologies. In this paper, it is concluded that the intelligent marketing system achieves personalized product recommendation by collecting user activity information on multiple platforms, constructing real-time user-profiles and extracting social image information, as well as using matrix decomposition and collaborative filtering algorithms for data analysis. Meanwhile, natural language processing technologies such as convolutional neural networks, recurrent neural network and attention mechanisms are used to enhance the interactive capability of the dialog system. However, Intelligent marketing systems face challenges such as data privacy and security, implementation of personalized and customized push, and neutrality of push content. This paper suggests continuous optimization of related technologies and the development of new functions during the implementation of the system, such as the introduction of multimodal interaction technology and attention to collecting after-sales user feedback, optimizing the database and algorithms, which will help solve the problems to a certain extent.