The online advertising platform serves as the intermediary between advertisers and users to facilitate the product trading process and generate profits. While the vitality of both the user and advertiser community is crucial to the long-term development of platforms, compared with the enduring efforts to improve user experience, little attention has been given to methods for attracting or retaining advertisers. To address this oversight, in this work, we investigate the first intelligent solution to the advertiser retention problem in online advertising. Motivated by advertiser surveys and data analytics, we first define a set of short-term ad performance metrics as the key retention metrics we need to optimize for advertisers. In consideration of the desirable properties of a retention framework, we propose an Auto-Coupon module to distribute impression-level virtual coupons to selected advertisers during the bidding process. We decompose the resulted retention optimization problem into two stages, and adopt feedback control as well as reinforcement learning methods to realize personalized coupon distribution. The offline experiments demonstrate the effectiveness of our methods in boosting the ad performance of churning advertisers, and the large-scale online A/B test further justifies the improvement in advertisers' willingness to stay in the platform.

In the field of auction design, leveraging deep learning to solve optimal auctions from sampled data has become a promising direction. However, real-world contexts often involve uncertain data, which would severely affect the auction performance, but it is lacking consideration in existing works. To address this challenge, we incorporate these uncertainties into auction design metrics, and frame this challenge as a robust data-driven auction design problem. To solve this problem, we first propose the GAT method, where we introduce the process of problem relaxation and transformation to address the non-differentiable variable presented in the original problem, and further propose an adversarial training algorithm to solve the mini-max problem after transformation. Moreover, to obtain moderately robust auctions, we propose two methods to select the robust coefficient, which provides guidance and insights for selecting robust auctions based on generalization and performance metrics. Finally, with the insights from the GAT method, we further propose the SAT method, where we employ a strict and unified IC constraint that extends from the GAT method, which provides strong IC guarantees and stable revenue in uncertain environments. Experiments on both constructed and real-world datasets show that our robust methods effectively improve the performance of auctions in terms of revenue and IC guarantees.

xisting online advertising systems generate high-quality ad recommendations through a complex online serving pipeline whenever a user's ad request arrives. However, our analyses on the display advertising system in Taobao show that this paradigm could lead to an inefficient utilization of computational resources. In this work, we propose an intelligent computation recycling framework called ComRecycle, which caches and reuses unexposed ad sets for repetitive ad requests to improve the computational resource utilization. We introduce fine-grained computation recycling strategies, and formulate the computation recycling decision as an online constrained optimization problem. Therefore, ComRecycle can achieve the goal of reducing computation costs while guaranteeing the same level of recommendation performance. Extensive offline experiments validate the correctness and effectiveness of ComRecycle. Our online A/B testing demonstrates that ComRecycle can save over 20% computational resources while maintaining the same system performance as the baseline.

Online advertising has become a core revenue driver for internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Classical auction mechanisms, such as GSP, rely on the independent CTR assumption and fail to account for the interplay among the displayed items, also called as externalities in economics. Recent advancements in learning-based auctions enable the encoding of high-dimensional contextual features. However, existing methods are limited by the ''prediction-before-allocation'' design paradigm, which models set-level externalities within candidate ads and fails to consider the context of the final allocation, leading to suboptimal results. In this work, we introduce Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal auction, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation, and reformulate incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, and effectively approximates the optimal auction with nearly maximal revenue and minimal regret.

Broad adoption of Online Travel Platforms (OTPs) has led to increasing interest in accurately predicting users' hotel purchase behavior, with price being a key influencer in user decision-making and receiving significant focus. In examining the hotel purchasing process, we identify a pervasive trend that users make extensive price comparisons before making decisions. Existing research primarily focuses on a hotel's own price, neglecting the complex dynamics of market-driven price competition. In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel's pricing competitiveness within the marketplace. Being independent of specific user preferences, MHPC can be applied to and improve various downstream operations in the online hotel industry, such as hotel ranking and pricing, ultimately benefiting hoteliers, users, and OTPs. Furthermore, a novel Hotel Price Competitiveness-aware Purchase Prediction Model (HP3M) is constructed by incorporating MHPC and demand dynamics into a multi-task learning framework, featuring three distinct submodules to encompass the tri-dimensional facets of MHPC. Extensive offline and online experiments demonstrate HP3M's effectiveness in predicting hotel purchase probability and enhancing the performance of hotel ranking and pricing compared to the state-of-the-art methods. HP3M has been fully deployed on Fliggy, a leading OTP in China, serving thousands of hoteliers and tens of millions of users.

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.

A contemporary feed application usually provides blended results of organic items and sponsored items~(ads) to users. Conventionally, ads are exposed at fixed positions. Such a fixed ad exposure strategy is inefficient due to ignoring users' personalized preferences towards ads. To this end,adaptive ad exposure is becoming an appealing strategy to boost the overall performance of the feed. However, existing approaches to implement the adaptive ad exposure strategy suffer from several limitations: 1) they usually fall into sub-optimal solutions because of only focusing on request-level optimization without consideration of the application-level performance and constraints, 2) they neglect the necessity of keeping the game-theoretical properties of ad auctions, and 3) they can hardly be deployed in large-scale applications due to high computational complexity. In this paper, we focus on the application-level performance optimization under hierarchical constraints in feeds and formulate adaptive ad exposure as a Dynamic Knapsack Problem. We propose Hierarchically Constrained Adaptive Ad Exposure~(HCA2E) that possesses the desirable game-theoretical properties, computational efficiency, and performance robustness. Comprehensive offline and online experiments on a leading e-commerce application demonstrate the performance superiority of HCA2E.