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原文摘要

Apps that monetize outbound (offsite) ad clicks have multiple post ad-click levers that influence user and business outcomes, including the landing page shown on the advertiser's site, whether the click resolves to a webpage or an app on the user's device, and whether it resolves to a native in-product surface or an external website. We refer to personalization over these levers as post ad-click destination automation. The optimal destination is heterogeneous across users, advertisers, placements, devices, and inferred intent, making static, one-size-fits-all destination policies suboptimal. We present Dynamic Click Destinations (DCD), a production personalization framework that selects a destination for each eligible offsite ad click to maximize Advertiser Value (AV), a value-weighted conversion outcome. DCD applies to any post-click destination decision where the system can choose among destination options at click time and downstream conversion feedback is available. We describe an evolution of learning objectives from post-conversion destination inference, to two-task destination-conditioned conversion prediction, to a single-task policy model that directly predicts which of two destinations is better for a given click, trained with Advantage Actor Critic (A2C)-inspired soft labels derived from a higher-capacity destination-conditioned "critic" model. In a web-vs-app case study, online A/B experiments at scale on a major advertising platform show that per-click personalization with DCD outperforms both static routing policies, with the single-task objective delivering the largest gains. These results highlight post ad-click destination as an important and under-explored lever in conversion optimization and demonstrate that personalized destination automation can outperform one-size-fits-all destination policies.