Generative recommendation formulates next-item prediction as conditional autoregressive generation over discrete Semantic IDs, enabling end-to-end recommendation over large-scale item spaces. However, most existing methods follow a history-as-context paradigm that repeatedly reconstructs user preference from behavior h...
Hui Qian, Chang-Fa Wu, Chang Liu et al.· arXiv.org· 0 citations
This work presents DREAM (Developing Recommender Engine with Agentic Methods), an autonomous optimization control architecture that adds a perception-aware, orchestrable, and auditable policy layer atop existing pipelines without replacing them, supporting agentic meta-control as a viable paradigm for industrial recomm...
Bin Zhang, Bo-Wen Zheng, Chao Yi et al.· 0 citations
Privileged Self-Distillation (PSD) is proposed, a framework that separates learning-time information from inference-time information and uses an advantage-reachability gate to focus distillation on teacher signals likely supported by the observed prefix, along with a momentum-averaged teacher for stable targets.
Jiakai Tang, Yang Zhang, See-Kiong Ng et al.· arXiv.org· 1 citation
RecGPT-V3 is presented, a stateful, hybrid-modal recommender that reasons over natural language for open-world knowledge and Semantic IDs (SIDs) for concrete item grounding and achieves consistent gains in large-scale online A/B tests.
RecVerse is presented, a GUI-grounded simulation agent that perceives pages through screenshots and produces faithful multi-turn trajectories and significantly outperforms existing baselines in both behavioral fidelity and intent consistency.
Jia-Kai Tang, Yan Mi, Jing Yu et al.· 0 citations
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