Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929456206053376 |
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| author | Yu, Qixuan Tang, Xirui Li, Feiyang Cao, Zinan |
| author_facet | Yu, Qixuan Tang, Xirui Li, Feiyang Cao, Zinan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks Yu, Qixuan Tang, Xirui Li, Feiyang Cao, Zinan Machine Learning Artificial Intelligence Information Retrieval Social and Information Networks 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. |
| title | Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks |
| topic | Machine Learning Artificial Intelligence Information Retrieval Social and Information Networks |
| url | https://arxiv.org/abs/2408.05245 |