Summary of Aigb: Generative Auto-bidding Via Conditional Diffusion Modeling, by Jiayan Guo and Yusen Huo and Zhilin Zhang and Tianyu Wang and Chuan Yu and Jian Xu and Yan Zhang and Bo Zheng
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
by Jiayan Guo, Yusen Huo, Zhilin Zhang, Tianyu Wang, Chuan Yu, Jian Xu, Yan Zhang, Bo Zheng
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 a novel paradigm for auto-bidding in online advertising called AI-Generated Bidding (AIGB), which uses generative modeling to overcome the limitations of traditional Markovian Decision Process (MDP) models. The proposed approach, DiffBid, is a conditional diffusion model that directly models the correlation between returns and trajectories, allowing it to perform well in long-horizon scenarios and highly random online environments. This leads to improved bid generation and increased return on investment (ROI). The paper presents extensive experiments on real-world datasets and an online A/B test on Alibaba’s advertising platform, demonstrating a 2.81% increase in Gross Merchandise Value (GMV) and 3.36% increase in ROI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces AI-Generated Bidding (AIGB), a new way to automatically generate bids for online ads. This helps advertisers by making sure they get the best deals. The old way of doing this, using something called Markovian Decision Process, had some big limitations. It couldn’t handle long-term scenarios very well and got unstable when dealing with random events. To solve this problem, the researchers created a new model called DiffBid. This model uses something called conditional diffusion modeling to make better bids. The results show that this new approach is really effective, increasing revenue by 2.81% and return on investment (ROI) by 3.36%. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
