In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.

As the main revenue source for search engines, sponsored search system retrieves and allocates ads to display on the search result pages. Click-through rate (CTR) prediction is a crucial task for search ads, due to it plays a key role in ranking and pricing of candidate ads. Commercial search engines typically model it as a classification problem and use machine learning models for CTR prediction. Recently deep learning based models have been proposed to learn latent representations of query-ad relevance and historical information to improve the accuracy, which follow the Embedding&MLP paradigm. As the learning feasible embeddings requires sufficient samples and these models rely mainly and heavily on textual features and sparse ID features, representations learning for new ads is inadequate and models are confronted with the cold-start problem. Meanwhile, as search ad offerings become increasingly complex, rich ads with various sizes, decorations and formats are growing rapidly. Due to the diverse ad extensions and layouts, rich ads pose new challenges for CTR prediction. To tackle these problems, in this paper, we propose an approach to improve the accuracy of CTR prediction by learning supplementary representations from three new aspects: the compositional components, the visual appearance and the relational structure of ads. This method can utilize straightforward and auxiliary information for new and rich ads, which can improve the expressive ability and the generalization of model greatly. We demonstrate the performance of this method on datasets obtained from a real sponsored search system in the offline environment. Experimental results show that our approach can improve the accuracy of CTR prediction and achieve superior performance compared to the baseline method, especially for the rich and new ads.

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 of key challenges. Finally, online A/B test and long-term grouping experiment demonstrate the advancement and effectiveness of this system.

As the main revenue source for search engines, sponsored search system retrieves and allocates ads to display on the search result pages. Keyword targeting is widely adopted by most sponsored search systems as the basic model for expressing the advertiser's business and retrieving related ads. In this targeting model, the advertiser should cautiously select lots of keywords relevant to their business to optimize their campaigns, and the sponsored search system retrieves ads based on the relevance between queries and keywords. However, since there is a huge inventory of possible queries and the new queries grow dramatically, it is a great challenge for advertisers to identify and collect lots of relevant bid keywords for their ads, and it also takes great effort to select and maintain high-quality keywords and set corresponding match types for them. In the meantime, the keyword targeting leads to a multi-stage retrieval architecture as it contains the matching between query and keywords and the matching between keywords and ads. The retrieval funnel based on keyword targeting cannot achieve straightforward and optimal matching between search queries and ads. Consequently, traditional keyword targeting method gradually becomes the bottleneck of optimizing advertisers' campaigns and improving the monetization of search ads.