  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.
