Modeling ultra-long user behavior sequences is crucial for industrial recommendation and online advertising, yet directly feeding thousands of historical actions into ranking models is computationally prohibitive, while truncation discards long-range signals. Existing lifelong-interest methods retrieve target-relevant...
Chengkai Huang, Yu-Bin Sheng, Liang Guo et al.· 0 citations
Long-sequence modeling is increasingly important in recommender systems for capturing users’ evolving and long-term interests. In advertising, however, user interaction histories are often highly sparse due to limited exposure opportunities, making ad-only behavior sequences insufficient for effective long-sequence rec...
Xian Hu, Ming Yue, Zhi-Xiang Feng et al.· Proceedings of the 20th ACM...· 0 citations
Auto-bidding has become a central component of modern advertising platforms. Recently, generative paradigms based on Decision Transformers (DT) have emerged as a promising alternative, modeling auto-bidding as sequence generation and using return-to-go (RTG) as a signal, thereby enabling long-horizon credit assignment...
Hongchang Wu, Weitong Ou, Hengquan Guo et al.· Proceedings of the 32nd ACM...· 0 citations
Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generative capacity to model complex, diverse user preferences. However, most existing approaches rely on an autoregressive paradigm that is subopti...
Chengyi Liu, Yong-Qi Zhou, Junwei Pan et al.· arXiv.org· 0 citations
DASH is a decision-aware user simulator that jointly generates thinking traces and predicts behavioral actions from heterogeneous cross-domain histories and tailors a rubric-based reward model that evaluates thinking traces along form, content, and logic for RL training.
