AiAds: Automated and Intelligent Advertising System for Sponsored Search

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.Comment: Accepted at ACM KDD 2019. arXiv admin note: text overlap with arXiv:1701.05946 by other author

The Ad Types Problem

The Ad Types Problem (without gap rules) is a special case of the assignment problem in which there are $k$ types of nodes on one side (the ads), and an ordered set of nodes on the other side (the slots). The edge weight of an ad $i$ of type $\theta$ to slot $j$ is $v_i\cdot \alpha^{\theta}_j$ where $v_i$ is an advertiser-specific value and each ad type $\theta$ has a discount curve $\alpha^{(\theta)}_{1} \ge \alpha^{(\theta)}_{2} \ge ... \ge 0$ over the slots that is common for ads of type $\theta$. We present two contributions for this problem: 1) we give an algorithm that finds the maximum weight matching that runs in $O(n^2(k + \log n))$ time for $n$ slots and $n$ ads of each type---cf. $O(kn^3)$ when using the Hungarian algorithm---, and 2) we show to do VCG pricing in asymptotically the same time, namely $O(n^2(k + \log n))$, and apply reserve prices in $O(n^3(k + \log n))$. The Ad Types Problem (with gap rules) includes a matrix $G$ such that after we show an ad of type $\theta_i$, the next $G_{ij}$ slots cannot show an ad of type $\theta_j$. We show that the problem is hard to approximate within $k^{1- \epsilon}$ for any $\epsilon > 0$ (even without discount curves) by reduction from Maximum Independent Set. On the positive side, we show a Dynamic Program formulation that solves the problem (including discount curves) optimally and runs in $O(k\cdot n^{2k + 1})$ time