Research on Algorithm of E-Commerce Recommendation System Based on Deep Learning

Personalized recommendation systems play a vital role in enhancing the user experience and boosting sales. This paper focuses on the research on the algorithm of e-commerce recommendation system based on deep learning, with special attention to advanced methods such as group recommendation, deep reinforcement learning, collaborative filtering and sequence recommendation. Firstly, for the problem of group recommendation, we propose a hybrid model that integrates user preference and group dynamics, which significantly improves the relevance and diversity of recommendation results by integrating individual users' behavior data and group interaction information. Secondly, in the field of deep reinforcement learning, we have designed an Adaptive Policy Network (ASPNet), which can automatically adjust the recommendation strategy based on the real-time feedback of users to optimize long-term returns. In addition, the traditional collaborative filtering technology has been extended to the deep collaborative filtering framework, which combines the powerful representation capabilities of neural networks to enhance the processing effect of sparse data. Finally, for sequence recommendation, the Time-Aware Recurrent Neural Network (TA-RNN) of attention mechanism is introduced to effectively capture the time dependence of user behavior and improve the accuracy of short-term interest prediction. The algorithm proposed in this paper shows superior performance on multiple public datasets, which provides new ideas and technical support for the intelligent development of e-commerce recommendation system.