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 revisits SID-based generative recommendation under an absolute-time temporal protocol that separates seen and unseen targets and diagnoses the cold item reachability at the token level, and shows that SID generation is compositional but not fully open-ended.
Jie Peng, Yanping Zheng, Zhewei Zhe et al.· arXiv.org· 1 citation
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
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.
Generative retrieval has become an increasingly adopted paradigm for industrial search, recommendation, and advertising systems, delivering significant online gains. Most existing work combines user behavior sequences with large language models (LLMs) to model user preferences. In practice, feature engineering remains...
Dan Ou, Gui Ling, Haokai Wan et al.· 0 citations
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