Hugging Face Trending Papers
AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System
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arXiv Machine Learning
Jul 31
PlatformBid: An Auto-Bidding Benchmark from a Unified Advertising Platform's Perspective
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 AI
Jul 29
HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
arXiv:2607. 24779v1 Announce Type: new Abstract: Online advertising bidding systems typically deploy multiple offline-trained expert models (e.
arXiv AI
Aug 17
AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution
arXiv:2608. 13833v1 Announce Type: cross Abstract: Conversational advertising aims to deliver useful ads within multi-turn assistant interactions.
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 AI
Sep 24
Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
The study investigates how Large Language Models (LLMs) acting as surrogate consumers are influenced by marketing pricing cues such as just‑below pricing and promotional framing. Using a tool called "Tool‑Lab" to trace information acquisition, the researchers found that when no cost is imposed, pricing cues rarely mislead LLMs, but when acquisition costs are introduced under a vague goal prompt, LLMs tend to omit important diagnostic attributes and make suboptimal choices similar to human heuristics. The findings suggest that marketing heuristics in AI‑driven shopping are shaped more by storefront information architecture than by inherent LLM limitations.
arXiv Computation and Language
Sep 10
AgenticGen: Reward-Guided Agentic Video Generation for Advertising
AgenticGen is a reward‑guided framework for generating advertising videos that splits the task into strategy selection and draft generation, allowing online business feedback to supervise each stage. It learns performance‑based rewards from accumulated online metrics and rubric‑based rewards aligned with human quality standards, then applies DPO and GRPO to refine policies. Offline tests confirm the reward models, and online A/B tests on TikTok show significant gains in CTR, CVR, and advertising value over a baseline.