Summary of Improved Adaboost Algorithm For Web Advertisement Click Prediction Based on Long Short-term Memory Networks, by Qixuan Yu et al.
Improved Adaboost Algorithm for Web Advertisement Click Prediction Based on Long Short-Term Memory Networks
by Qixuan Yu, Xirui Tang, Feiyang Li, Zinan Cao
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an improved Adaboost algorithm based on Long Short-Term Memory Networks (LSTMs) for predicting user clicks on web page advertisements. The new model is compared to several common machine learning algorithms, showing a significant improvement in prediction accuracy, with a 13.6% increase over the highest-performing base model. The improved algorithm achieves an accuracy of 92%, outperforming other models in metrics such as recall and F1 score. The study also evaluates the model’s generalisation ability, finding only a 1.7% difference between training and test sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new machine learning algorithm that can predict user clicks on online ads with high accuracy. By using special type of neural network called LSTMs, the algorithm is able to capture complex patterns in how users behave. The new algorithm performs much better than other common algorithms, making it useful for real-world applications. |
Keywords
» Artificial intelligence » F1 score » Machine learning » Neural network » Recall
