Dagui Chen

In online e-commerce platforms, sponsored ads are always mixed with non-sponsored organic content (recommended items). To guarantee user experience, online platforms always impose strict limitations on the number of ads displayed, becoming the bottleneck for advertising revenue. To boost advertising space, we introduce a novel advertising business paradigm called Augment Advertising, where once a user clicks on a leading ad on the main page, instead of being shown the corresponding products, a collection of mini-detail ads relevant to the clicked ad is displayed. A key component for augment advertising is to design ad auctions to jointly select leading ads on the main page and mini-detail ads on the augment ad page. In this work, we decouple the ad auction into a two-stage auction, including a leading ad auction and a mini-detail ad auction. We design the Potential Generalized Second Price (PGSP) auction with Symmetric Nash Equilibrium (SNE) for leading ads, and adopt GSP auction for mini-detail ads. We have deployed augment advertising on Taobao advertising platform, and conducted extensive offline evaluations and online A/B tests. The evaluation results show that augment advertising could guarantee user experience while improving the ad revenue and the PGSP auction outperforms baselines in terms of revenue and user experience in augment advertising.

Most e-commerce product feeds provide blended results of advertised products and recommended products to consumers. The underlying advertising and recommendation platforms share similar if not exactly the same set of candidate products. Consumers' behaviors on the advertised results constitute part of the recommendation model's training data and therefore can influence the recommended results. We refer to this process as Leverage. Considering this mechanism, we propose a novel perspective that advertisers can strategically bid through the advertising platform to optimize their recommended organic traffic. By analyzing the real-world data, we first explain the principles of Leverage mechanism, i.e., the dynamic models of Leverage. Then we introduce a novel Leverage optimization problem and formulate it with a Markov Decision Process. To deal with the sample complexity challenge in model-free reinforcement learning, we propose a novel Hybrid Training Leverage Bidding (HTLB) algorithm which combines the real-world samples and the emulator-generated samples to boost the learning speed and stability. Our offline experiments as well as the results from the online deployment demonstrate the superior performance of our approach.