AIGB: Generative Auto-bidding via Conditional Diffusion Modeling

• URL: http://arxiv.org/abs/2405.16141v4
• Date: Tue, 08 Oct 2024 07:02:01 GMT
• Title: AIGB: Generative Auto-bidding via Conditional Diffusion Modeling
• Authors: Jiayan Guo, Yusen Huo, Zhilin Zhang, Tianyu Wang, Chuan Yu, Jian Xu, Yan Zhang, Bo Zheng,
• Abstract summary: This paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. Experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid.
• Score: 26.283427427408085
• License:
• Abstract: Auto-bidding plays a crucial role in facilitating online advertising by automatically providing bids for advertisers. Reinforcement learning (RL) has gained popularity for auto-bidding. However, most current RL auto-bidding methods are modeled through the Markovian Decision Process (MDP), which assumes the Markovian state transition. This assumption restricts the ability to perform in long horizon scenarios and makes the model unstable when dealing with highly random online advertising environments. To tackle this issue, this paper introduces AI-Generated Bidding (AIGB), a novel paradigm for auto-bidding through generative modeling. In this paradigm, we propose DiffBid, a conditional diffusion modeling approach for bid generation. DiffBid directly models the correlation between the return and the entire trajectory, effectively avoiding error propagation across time steps in long horizons. Additionally, DiffBid offers a versatile approach for generating trajectories that maximize given targets while adhering to specific constraints. Extensive experiments conducted on the real-world dataset and online A/B test on Alibaba advertising platform demonstrate the effectiveness of DiffBid, achieving 2.81% increase in GMV and 3.36% increase in ROI.

Related papers

• Transfer Learning for Diffusion Models [43.10840361752551]
  Diffusion models consistently produce high-quality synthetic samples. They can be impractical in real-world applications due to high collection costs or associated risks. This paper introduces the Transfer Guided Diffusion Process (TGDP), a novel approach distinct from conventional finetuning and regularization methods.
  arXiv  Detail & Related papers  (2024-05-27T06:48:58Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation [59.184980778643464]
  Fine-tuning Diffusion Models remains an underexplored frontier in generative artificial intelligence (GenAI) In this paper, we introduce an innovative technique called self-play fine-tuning for diffusion models (SPIN-Diffusion) Our approach offers an alternative to conventional supervised fine-tuning and RL strategies, significantly improving both model performance and alignment.
  arXiv  Detail & Related papers  (2024-02-15T18:59:18Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• A Survey on Generative Diffusion Model [75.93774014861978]
  Diffusion models are an emerging class of deep generative models. They have certain limitations, including a time-consuming iterative generation process and confinement to high-dimensional Euclidean space. This survey presents a plethora of advanced techniques aimed at enhancing diffusion models.
  arXiv  Detail & Related papers  (2022-09-06T16:56:21Z)
• ROI Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning [34.82004227655201]
  We specialize in ROI-Constrained Bidding in non-stationary markets. Based on a Partially Observable Constrained Markov Decision Process, we propose the first hard barrier solution to accommodate non-monotonic constraints. Our method exploits a parameter-free indicator-augmented reward function and develops a Curriculum-Guided Bayesian Reinforcement Learning framework.
  arXiv  Detail & Related papers  (2022-06-10T17:30:12Z)
• VFed-SSD: Towards Practical Vertical Federated Advertising [53.08038962443853]
  We propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data. Our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.
  arXiv  Detail & Related papers  (2022-05-31T17:45:30Z)
• Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising [2.562910030418378]
  The purpose of Inventory Pricing is to bid the right prices to online ad opportunities, which is crucial for a Demand-Side Platform (DSP) to win auctions in Real-Time Bidding (RTB) Most of the previous works made strong assumptions on the distribution form of the winning price, which reduced their accuracy and weakened their ability to make generalizations. We propose a novel loss function, Neighborhood Likelihood Loss (NLL), collaborating with a proposed framework, Arbitrary Distribution Modeling (ADM) to predict the winning price distribution under censorship.
  arXiv  Detail & Related papers  (2021-10-26T11:40:00Z)
• Online and Scalable Model Selection with Multi-Armed Bandits [0.0]
  We present Automatic Model Selector (AMS), a system for scalable online selection of bidding strategies based on real-world performance metrics. AMS allocates the most traffic to the best-performing models while decreasing traffic to those with poorer online performance. In live-traffic tests on multiple ad campaigns, the AMS system proved highly effective at improving ad campaign performance.
  arXiv  Detail & Related papers  (2021-01-25T20:12:52Z)
• MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding [47.555870679348416]
  We propose a Multi-ecTive Actor-Critics algorithm named MoTiAC for the problem of bidding optimization with various goals. Unlike previous RL models, the proposed MoTiAC can simultaneously fulfill multi-objective tasks in complicated bidding environments.
  arXiv  Detail & Related papers  (2020-02-18T07:16:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.