OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation

• URL: http://arxiv.org/abs/2603.02999v2
• Date: Wed, 04 Mar 2026 09:01:13 GMT
• Title: OneRanker: Unified Generation and Ranking with One Model in Industrial Advertising Recommendation
• Authors: Dekai Sun, Yiming Liu, Jiafan Zhou, Xun Liu, Chenchen Yu, Yi Li, Huan Yu, Jun Zhang,
• Abstract summary: We propose OneRanker, achieving architectural-level deep integration of generation and ranking.<n>We construct a coarse-to-fine collaborative target awareness mechanism.<n>The full deployment on Tencent's WeiXin channels advertising system has shown a significant improvement in key business metrics.
• Score: 16.27240743307534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The end-to-end generative paradigm is revolutionizing advertising recommendation systems, driving a shift from traditional cascaded architectures towards unified modeling. However, practical deployment faces three core challenges: the misalignment between interest objectives and business value, the target-agnostic limitation of generative processes, and the disconnection between generation and ranking stages. Existing solutions often fall into a dilemma where single-stage fusion induces optimization tension, while stage decoupling causes irreversible information loss. To address this, we propose OneRanker, achieving architectural-level deep integration of generation and ranking. First, we design a value-aware multi-task decoupling architecture. By leveraging task token sequences and causal mask, we separate interest coverage and value optimization spaces within shared representations, effectively alleviating target conflicts. Second, we construct a coarse-to-fine collaborative target awareness mechanism, utilizing Fake Item Tokens for implicit awareness during generation and a ranking decoder for explicit value alignment at the candidate level. Finally, we propose input-output dual-side consistency guarantees. Through Key/Value pass-through mechanisms and Distribution Consistency (DC) Constraint Loss, we achieve end-to-end collaborative optimization between generation and ranking. The full deployment on Tencent's WeiXin channels advertising system has shown a significant improvement in key business metrics (GMV - Normal +1.34\%), providing a new paradigm with industrial feasibility for generative advertising recommendations.

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