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.