# AiAds: Automated and Intelligent Advertising System for Sponsored Search

arXiv: https://arxiv.org/abs/1907.12118 · HTML: https://mlsanity.com/paper/1907.12118/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://mlsanity.com/paper/2106.06224/) (2021)
- [JIZHI: A Fast and Cost-Effective Model-As-A-Service System for Web-Scale Online Inference at Baidu](https://mlsanity.com/paper/2106.01674/) (2021)
- [Quantized Training of Gradient Boosting Decision Trees](https://mlsanity.com/paper/2207.09682/) (2022)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [Generative Large-Scale Pre-trained Models for Automated Ad Bidding Optimization](https://mlsanity.com/paper/2508.02002/) (2025)
- [Autobidding Arena: unified evaluation of the classical and RL-based autobidding algorithms](https://mlsanity.com/paper/2510.19357/) (2025)
- [PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective](https://mlsanity.com/paper/2607.27265/) (2026)
- [Mechanism Design for Ad Auctions with Display Prices](https://mlsanity.com/paper/2303.13192/) (2023)
- [Strategic Bidding in 6G Spectrum Auctions with Large Language Models](https://mlsanity.com/paper/2604.24156/) (2026)
- [OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios](https://mlsanity.com/paper/2609.21550/) (2026)

## 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.