GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model

Ziheng Ni; Congcong Liu; Cai Shang; Yiming Sun; Junjie Li; Zhiwei Fang; Guangpeng Chen; Jian Li; Zehua Zhang; Changping Peng; Zhangang Lin; Ching Law; Jingping Shao

arXiv · 2601.02361

GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model

Ziheng Ni·Congcong Liu·Cai Shang·Yiming Sun·Junjie Li·Zhiwei Fang·Guangpeng Chen·Jian Li·Zehua Zhang·Changping Peng·Zhangang Lin·Ching Law·Jingping Shao

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Abstract

The ranking stage serves as the central optimization and allocation hub in advertising systems, governing economic value distribution through eCPM and orchestrating the user-centric blending of organic and advertising content. Prevailing ranking models often rely on fragmented modules and hand-crafted features, limiting their ability to interpret complex user intent. This challenge is further amplified in location-based services such as food delivery, where user decisions are shaped by dynamic spatial, temporal, and individual contexts. To address these limitations, we propose a novel generative framework that reframes ranking as a context comprehension task, modeling heterogeneous signals in a unified architecture. Our architecture consists of two core components: the Generative Contextual Encoder (GCE) and the Generative Contextual Fusion (GCF). The GCE comprises three specialized modules: a Personalized Context Enhancer (PCE) for user-specific modeling, a Collective Context Enhancer (CCE) for group-level patterns, and a Dynamic Context Enhancer (DCE) for real-time situational adaptation. The GCF module then seamlessly integrates these contextual representations through low-rank adaptation. Extensive experiments confirm that our method achieves significant gains in critical business metrics, including click-through rate and platform revenue. We have successfully deployed our method on a large-scale food delivery advertising platform, demonstrating its substantial practical impact. This work pioneers a new perspective on generative recommendation and highlights its practical potential in industrial advertising systems.

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Ziheng Ni, Congcong Liu, Cai Shang, Yiming Sun, Junjie Li, Zhiwei Fang, Guangpeng Chen, Jian Li, Zehua Zhang, Changping Peng, Zhangang Lin, Ching Law, Jingping Shao. 2025-10-15. GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model. https://arxiv.org/abs/2601.02361

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