AiAds: Automated and Intelligent Advertising System for Sponsored Search
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Abstract (arXiv)
Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers. In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several key challenges.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2019.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 3.
10 papers.
- 2021A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertisingcited by 22
- 2021JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baiducited by 10
- 2022Quantized Training of Gradient Boosting Decision Treescited by 6
- 2025Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Alignercited by 5
- 2025Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimizationcited by 3
- 2025Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithmscited by 1
- 2026PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspectivecited by 1
- 2023Mechanism Design for Ad Auctions with Display Pricescited by 0
- 2026Strategic Bidding in 6G Spectrum Auctions with Large Language Modelscited by 0
- 2026OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarioscited by 0
- 2026OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios18 Sep 2026
- 2026PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective29 Jul 2026
- 2026Strategic Bidding in 6G Spectrum Auctions with Large Language Models27 Apr 2026
- 2025Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms22 Oct 2025
- 2025Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner3 Sep 2025
- 2025Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization4 Aug 2025
- 2023Mechanism Design for Ad Auctions with Display Prices23 Mar 2023
- 2022Quantized Training of Gradient Boosting Decision Trees20 Jul 2022
- 2021A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising11 Jun 2021
- 2021JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu3 Jun 2021