{"ID":2855067,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02361","arxiv_id":"2601.02361","title":"GCRank: A Generative Contextual Comprehension Paradigm for Takeout Ranking Model","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.","short_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, limitin...","url_abs":"https://arxiv.org/abs/2601.02361","url_pdf":"https://arxiv.org/pdf/2601.02361v1","authors":"[\"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\"]","published":"2025-10-15T08:42:04Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}