arXiv AI
Jul 9
Ad Headline Generation using Self-Critical Masked Language Model
arXiv:2607. 06818v1 Announce Type: cross Abstract: For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers.
COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation
arXiv:2607. 08071v1 Announce Type: cross Abstract: Online ads are essential to all businesses and ad headlines are one of their core creative component.
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arXiv Computer Vision
3d ago
AD-Relight: Training-Free Banner Relighting via Illumination Translation with Diffusion Priors
arXiv:2604.24407v2 Announce Type: replace Abstract: The recent surge in content consumption through streaming services has driven a growing demand for personalized content. Personalized advertisement...
arXiv Machine Learning
Jul 17
Adaptive Ad Load Design for Sponsored Search Markets: Evidence, Theory, and Deployment
arXiv:2607. 14418v1 Announce Type: new Abstract: Ad-load design is a central supply-side decision in sponsored search: more sponsored slots can raise revenue, but may crowd out organic results and degrade user outcomes.
arXiv Computation and Language
3d ago
Interactor: Agentic RL oriented Iterative Creation for Ad Description Generation in Sponsored Search
arXiv:2606.15911v2 Announce Type: replace Abstract: This paper focuses on automatically generating informative ad descriptions in sponsored search. Unlike ad titles which are usually optimized to att...
arXiv AI
Jul 24
PromptPack: Scaling LLM Annotation Agents for Online Recommendation
arXiv:2607. 20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives.
arXiv Machine Learning
Aug 28
Token-Level Advertising
The paper introduces the Latent Advertiser Mixture Auction (LAMA), a token‑level advertising framework that integrates advertiser influence directly into the text generation process. Advertisers provide local continuation values that shape next‑token policies, and the platform decodes these through a latent mixture while updating an allocation posterior. LAMA is shown to satisfy Markov DSIC and IR, achieve near‑optimal KL‑regularized welfare, and, in proof‑of‑concept experiments on commercial‑search queries, improve platform welfare and revenue without compromising user‑facing response quality.
arXiv AI
Aug 24
ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising
arXiv:2412.06167v2 Announce Type: replace Abstract: In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. W...
arXiv Machine Learning
Aug 12
MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction
arXiv:2608. 10562v1 Announce Type: new Abstract: Not all clicks are equal.
arXiv Machine Learning
Sep 22
IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu
arXiv:2603.01590v2 Announce Type: replace-cross Abstract: Content-driven platforms such as Xiaohongshu often leverage click-through rate (CTR) prediction models for recommendation. However, these mod...
arXiv Machine Learning
Aug 12
CADET: Context-Conditioned Ads CTR Prediction With a Decoder-Only Transformer
arXiv:2602. 11410v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction is fundamental to online advertising systems.
arXiv AI
Sep 18
Watermarking Diffusion Language Models
The paper introduces the first watermark designed specifically for diffusion language models (DLMs), which generate tokens in arbitrary order unlike traditional autoregressive models. It overcomes the challenge of missing prior tokens by applying the watermark in expectation over the context and promoting tokens that strengthen the watermark when used as context. Experiments show a >99% true positive rate with minimal quality loss and comparable robustness to existing autoregressive watermarks.
arXiv Machine Learning
Jul 28
SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads
arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).