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.

Aggregate monitoring over data streams is attracting more and more attention in research community due to its broad potential applications. Existing methods suffer two problems, 1) The aggregate functions which could be monitored are restricted to be first-order statistic or monotonic with respect to the window size. 2) Only a limited number of granularity and time scales could be monitored over a stream, thus some interesting patterns might be neglected, and users might be misled by the incomplete changing profile about current data streams. These two impede the development of online mining techniques over data streams, and some kind of breakthrough is urged. In this paper, we employed the powerful tool of fractal analysis to enable the monitoring of both monotonic and non-monotonic aggregates on time-changing data streams. The monotony property of aggregate monitoring is revealed and monotonic search space is built to decrease the time overhead for accessing the synopsis from O(m) to O(logm), where m is the number of windows to be monitored. With the help of a novel inverted histogram, the statistical summary is compressed to be fit in limited main memory, so that high aggregates on windows of any length can be detected accurately and efficiently on-line. Theoretical analysis show the space and time complexity bound of this method are relatively low, while experimental results prove the applicability and efficiency of the proposed algorithm in different application settings.