Generative modeling, particularly Decision Transformers, shows promise in auto-bidding strategies. However, it struggles with limited exploration in static offline datasets, vulnerability to dynamic environmental shifts, and a reliance on heuristic return-to-go (RTG) specifications that may yield suboptimal performance...
Lu-Zhan Yuan, Chao Mi, Xia-Hao Shi et al.· Proceedings of the 35th ACM...· 0 citations
LangBP is proposed, a hierarchical framework for language-guided joint bidding and pricing that outperforms strong baselines, and online A/B tests further demonstrate business gains in real-world deployment on a large-scale e-commerce platform.
Jian-Qing Ding, Chuan Yang, Linghui Meng et al.· 0 citations
Industrial generative recommendation systems operating over discrete Semantic IDs (SIDs) are largely behavior-driven, and thus struggle to proactively activate latent demand before explicit user signals emerge, leading to intent cold-start. To address this, we propose RIGER (Reasoning-grounded Intent injection for GE n...
Xusong Chen, Peini Guo, Fang Liu et al.· Annual International ACM SIG...· 0 citations
This paper proposes DPEO (Dynamic Preference Evolution Optimization), a co-evolutionary framework that transforms CTR modeling into a dynamic policy contention task, enabling the sub-learners to serve as alternating evolutionary benchmarks and 'self-evolve' toward the global optimum.
Kun Yao, Congcong Liu, Ziheng Ni et al.· Annual International ACM SIG...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
