This paper explores an improved Adaboost algorithm based on Long Short-Term Memory Networks (LSTMs), which aims to improve the prediction accuracy of user clicks on web page advertisements. By comparing it with several common machine learning algorithms, the paper analyses the advantages of the new model in ad click prediction. It is shown that the improved algorithm proposed in this paper performs well in user ad click prediction with an accuracy of 92%, which is an improvement of 13.6% compared to the highest of 78.4% among the other three base models. This significant improvement indicates that the algorithm is more capable of capturing user behavioural characteristics and time series patterns. In addition, this paper evaluates the model's performance on other performance metrics, including accuracy, recall, and F1 score. The results show that the improved Adaboost algorithm based on LSTM is significantly ahead of the traditional model in all these metrics, which further validates its effectiveness and superiority. Especially when facing complex and dynamically changing user behaviours, the model is able to better adapt and make accurate predictions. In order to ensure the practicality and reliability of the model, this study also focuses on the accuracy difference between the training set and the test set. After validation, the accuracy of the proposed model on these two datasets only differs by 1.7%, which is a small difference indicating that the model has good generalisation ability and can be effectively applied to real-world scenarios.

翻译：本文探讨了一种基于长短期记忆网络（LSTM）的改进Adaboost算法，旨在提升网页广告用户点击预测的准确性。通过与多种常见机器学习算法进行对比，本文分析了新模型在广告点击预测中的优势。研究表明，本文提出的改进算法在用户广告点击预测中表现优异，准确率达到92%，相较于其他三种基础模型中最高的78.4%提升了13.6%。这一显著改进表明该算法更能捕捉用户行为特征与时间序列模式。此外，本文评估了模型在其他性能指标上的表现，包括精确率、召回率与F1分数。结果显示基于LSTM的改进Adaboost算法在所有这些指标上均显著领先于传统模型，进一步验证了其有效性与优越性。尤其在面对复杂动态变化的用户行为时，该模型能够更好地适应并做出准确预测。为确保模型的实用性与可靠性，本研究还重点关注了训练集与测试集之间的准确率差异。经验证，所提模型在这两个数据集上的准确率仅相差1.7%，这一微小差异表明模型具有良好的泛化能力，能够有效应用于实际场景。