arXiv:2510. 15238v2 Announce Type: replace-cross Abstract: Optimizing a single advertising campaign across heterogeneous channels is a central challenge in industrial autobidding.
arXiv:2605. 09448v2 Announce Type: replace Abstract: We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints.
arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.
arXiv:2607. 27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Platform (DSP) bidding for advertisers, and Ad Exchange conducting auctions between them.
arXiv:2608. 00123v1 Announce Type: cross Abstract: LLM-native advertising embeds sponsored content directly into model-generated responses, shifting the unit of sale from a fixed slot to a moment within an evolving conversation.
arXiv:2607. 24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.
arXiv:2608. 03705v1 Announce Type: new Abstract: Real-time bidding (RTB) ad exchanges typically forward nearly all incoming requests to demand-side platforms (DSPs), even though only a small fraction receive bids.
arXiv:2608. 12719v1 Announce Type: cross Abstract: Routing each query to a cost-effective large language model (LLM) is critical for balancing quality and cost, yet most routers rely on a centralized task center to predict model performance, creating an information-risk mismatch and a scalability bottleneck as the model pool grows.
The paper introduces BAFF, a Bid‑Aware Filter Family that mitigates training data interference in real‑time bidding (RTB) A/B tests by applying (k,l)-parameterized hard filters to control bias from ad‑ranking and bid‑pricing disagreements. It proposes a three‑stage online measurement protocol to evaluate data‑sharing strategies against an interference‑free reference model. Experiments show that BAFF variants outperform both log‑sharing and log‑splitting in offline simulations and live DSP deployments, preserving key business metrics more closely.
The paper extends the Expected Cost-per-Mille (eCPM) framework to handle auction and hybrid "Auction with Buy It Now" (ABIN) listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing an additional impression for items whose prices evolve dynamically. This unified ranking objective allows fixed-price, auction, and ABIN listings to be compared and ranked together. A production implementation was tested via online A/B experiments on a large e-commerce platform, yielding positive revenue gains and statistically significant improvements in user metrics, leading to deployment in production.
OneBid is a unified auto‑bidding foundation model that consolidates diverse cost‑per‑X (oCPX) advertising scenarios into a single framework. It builds on Decision Transformer by conditioning on two atomic signals—Return‑to‑Go for conversion value and Cost‑to‑Go for cost ratio—and incorporates value‑aware regularization. A sequence‑level Mixture‑of‑Experts architecture captures cross‑scenario knowledge while preserving low latency, and a Critic‑guided Relative Offline Policy optimization (CROP) aligns the backbone with scenario‑specific preferences without unsafe online exploration. In production at Kuaishou, OneBid achieved a 2.2% overall ADVV increase and up to 13.1% in the ROAS scenario.
arXiv:2505.02796v3 Announce Type: replace-cross Abstract: We study budget pacing in repeated first-price auctions when an advertiser's private-value distributions change over time and the stationary...
arXiv:2602. 08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA).