Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in Feed

Xuejian Li, Ze Wang, Bingqi Zhu, Fei He, Yongkang Wang, Xingxing Wang

Abstract: E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad revenue and gross merchandise volume (GMV). Specifically, the ad auction determines which ad is displayed and the corresponding payment, while the ad allocation decides the display positions of the advertisement and organic items. The prevalent methods of segregating the ad auction and allocation into two distinct stages face two problems: 1) Ad auction does not consider externalities, such as the influence of actual display position and context on ad Click-Through Rate (CTR); 2) The ad allocation, which utilizes the auction-winning ad's payment to determine the display position dynamically, fails to maintain incentive compatibility (IC) for the advertisement. For instance, in the auction stage employing the traditional Generalized Second Price (GSP), even if the winning ad increases its bid, its payment remains unchanged. This implies that the advertisement cannot secure a better position and thus loses the opportunity to achieve higher utility in the subsequent ad allocation stage. Previous research often focused on one of the two stages, neglecting the two-stage problem, which may result in suboptimal outcomes.Therefore, this paper proposes a deep automated mechanism that integrates ad auction and allocation, ensuring both IC and Individual Rationality (IR) in the presence of externalities while maximizing revenue and GMV. The mechanism takes candidate ads and the ordered list of organic items as input. For each candidate ad, several candidate allocations are generated by inserting the ad in different positions of the ordered list of organic items. For each candidate allocation, a list-wise model takes the entire allocation as input and outputs the predicted result for each ad and organic item to model the global externalities. Finally, an automated auction mechanism, modeled by deep neural networks, is executed to select the optimal allocation. Consequently, this mechanism simultaneously decides the ranking, payment, and display position of the ad. Furthermore, the proposed mechanism results in higher revenue and GMV than state-of-the-art baselines in offline experiments and online A/B tests.