Nikhil Bhale | Search arXiv

arXiv2026

Improving Ads Ranking Models with Auxiliary Supervision: Techniques and Insights

Accurate prediction of post-click Conversion Rate (CVR) is essential for personalized ad delivery in the advertising industry. Traditional ads ranking models rely on conversion events (e.g., a user purchasing an item) as ground truth labels for training. However, these conversion events are often sparse or missing, which limits the effectiveness of model learning. The primary contributions of this paper are: (1) a case study on the use of auxiliary supervision from a diverse set of user events and metadata signals beyond traditional conversion events, and (2) advanced modeling techniques designed to maximize the value of these signals. We detail architectural innovations such as linearized labels for improved auxiliary signal precision, a split architecture to facilitate training task decomposition, and a novel Label Combination (LaCo) approach for robustly managing noisy auxiliary data. Empirical results demonstrate that these methods deliver measurable improvements in ranking models, underscoring their versatility and effectiveness across recommendation systems and related domains.

cs.IR↗

arXiv2026

Dynamic Click Destinations: Personalizing Post Ad-Click Landing Experiences

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.

cs.IR↗