Industrial recommendation ranking models typically scale along two modeling axes: feature interaction over heterogeneous user, item, context, and cross features, and sequence modeling over long, informative, and multi-type user behavior histories. We find that scaling either capability in isolation is insufficient, as...
Yun-Tao Zheng, Miao Zhang, Ya-Dong Ding et al.· 0 citations
This paper proposes goal-conditioned supervised learning (GCSL) as an offline fine-tuning framework for LLMs and proposes natural-language goal representations to further connect these patterns to the LLM's pretrained knowledge and generalization capabilities.
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
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