Optimization of Personalized Recommendation Algorithm for E-Commerce Platforms Based on Deep Learning

Recommendation services that precisely meet user needs have become the core element for e-commerce platforms to attract and retain users and increase sales. In such a context, building an efficient and personalized recommendation system is undoubtedly crucial for e-commerce platforms. In this paper, a hybrid recommendation model combining convolutional neural network (CNN) and recurrent neural network (RNN) is designed through deep learning algorithms. CNN is used to extract commodity features, and RNN is used to capture the temporal dependence in the user behavior sequence. Research shows that the hybrid recommendation model not only effectively solves the problems of data sparsity and cold start in traditional recommendation systems, but also significantly improves the accuracy and efficiency of the recommendation system, providing new ideas and methods for intelligent recommendation systems.