E-Commerce Recommendation Algorithm Based on Data Augmentation and Attention Mechanism

Aiming at the limitations of traditional digital mining algorithms in the research of e-commerce recommendation algorithms, a recommendation algorithm research scheme based on data augmentation and attention mechanism was proposed. Firstly, the influencing factors is accurately located through iterative statistical theory, and the indicators is reasonably divided to reduce interference, and the data augmentation and attention mechanism is used to construct a recommendation algorithm research scheme. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional digital mining algorithm in terms of the research accuracy of the recommendation algorithm and the processing time of influencing factors, and has obvious advantages. The research of recommendation algorithm plays an extremely important role in the era of intelligent e-commerce, which can accurately predict and optimize the growth characteristics and product generation in the era of intelligent e-commerce. However, traditional digital mining algorithms have certain limitations in solving the simulation problem of e-commerce recommendation, especially when dealing with complex problems. In this paper, this paper proposes a recommendation algorithm based on data augmentation and attention mechanism to better solve this problem. The scheme accurately locates the influencing factors through iterative statistical theory, so as to determine the division of indicators, and uses data augmentation and attention mechanism to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on data augmentation and attention mechanism can better solve the limitations of traditional digital mining algorithms and improve the accuracy and efficiency of simulation in the research of e-commerce recommendation algorithms.