Summary of Ctr-kan: Kan For Adaptive High-order Feature Interaction Modeling, by Yunxiao Shi et al.
CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling
by Yunxiao Shi, Wujiang Xu, Haimin Zhang, Qiang Wu, Yongfeng Zhang, Min Xu
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 proposed framework, CTR-KAN, is an adaptive solution for efficiently modeling high-order feature interactions in click-through rate (CTR) prediction tasks. Building upon the Kolmogorov-Arnold Network (KAN) paradigm, CTR-KAN addresses limitations of traditional methods by introducing a lightweight architecture that reduces computational complexity and supports embedding-based feature representations. Key enhancements include guided symbolic regression to capture multiplicative relationships, allowing for effective modeling of high-order interactions. The framework achieves state-of-the-art predictive accuracy with significantly lower computational costs, making it a powerful tool for real-world CTR prediction scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CTR-KAN is a new way to predict how likely someone will click on an online ad. Right now, most methods have trouble balancing how well they can guess and how fast they can do it. To fix this, the researchers created a framework called CTR-KAN that’s better at both. It uses a special kind of network that learns from data and is good at finding patterns in numbers. This helps it make predictions more accurately. The team also added some extra tricks to help the model learn about different relationships between features. The results show that this new approach can predict clicks really well, but also doesn’t use up too many computer resources. |
Keywords
» Artificial intelligence » Embedding » Regression
