Summary of An Accuracy Improving Method For Advertising Click Through Rate Prediction Based on Enhanced Xdeepfm Model, by Xiaowei Xi et al.
An accuracy improving method for advertising click through rate prediction based on enhanced xDeepFM model
by Xiaowei Xi, Song Leng, Yuqing Gong, Dalin Li
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 click-through rate (CTR) prediction model based on the xDeepFM architecture to address challenges like data sparsity, class imbalance, and neglecting important feature interactions. The new model integrates a multi-head attention mechanism to simultaneously focus on different aspects of feature interactions, enhancing its ability to learn intricate patterns without increasing complexity. Additionally, it replaces the linear model with a Factorization Machine (FM) model to handle high-dimensional sparse data by capturing both first-order and second-order feature interactions. Experimental results on the Criteo dataset show significant improvements in AUC and Logloss metrics over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make better predictions about whether people will click on ads or not. They’re trying to do this by making a new model that can learn patterns from data, but they’re having some trouble because the data is hard to work with (it’s sparse and has imbalances). The new model uses something called attention to look at different parts of the data together, which helps it learn more complicated things. They also replace one part of the old model with a new thing called Factorization Machines, which makes it better at handling big datasets. When they tested this new model on some real data, it did way better than other models that are already really good. |
Keywords
» Artificial intelligence » Attention » Auc » Multi head attention
