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arXiv · 2607.24439

Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence

Ruochen Yang·Shuang Wen·Pengbo Xu·Yusheng Huang·Jiangxia Cao·Shuang Yang·Zhaojie Liu·Jiawei Sheng·Tingwen Liu
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Abstract

Modern industrial recommendation systems typically separate recall and ranking into two independent stages. Although this cascade supports corpus-level retrieval and fine-grained multi-objective scoring, it causes objective inconsistency, information loss at the candidate hand-off, and redundant user-side context computation. Meanwhile, the generative recall and ranking scaling share a common Transformer-based modeling philosophy, where architectural consistency creates a natural opportunity for unified integration. However, direct sharing remains challenging since the two tasks require different information visibility and optimization methods. Therefore, we propose \textbf{UniR$^2$}, a \textbf{Uni}fied decoder-only Transformer that unifies Generative \textbf{R}ecall and Multi-Objective \textbf{R}anking within a single heterogeneous sequence comprising user context, SID trajectory, and item features. Within this sequence, the generated trajectory serves as a representation bridge between recall and ranking, where Dual-Query Prefix-Causal Attention provides task-specific visibility. The two tasks share the base attention weights but retain separate optimization boundaries, with ranking-side LoRA preserving ranking adaptability without disrupting the generative backbone. Extensive offline experiments on large-scale industrial data demonstrate the effectiveness and efficiency of UniR$^2$ for both recall and ranking. Long-term online A/B tests on Kuaishou platform further show consistent positive gains, validating the practicality of unified model in large-scale recommendation systems.

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Ruochen Yang, Shuang Wen, Pengbo Xu, Yusheng Huang, Jiangxia Cao, Shuang Yang, Zhaojie Liu, Jiawei Sheng, Tingwen Liu. 2026-07-27. Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence. https://arxiv.org/abs/2607.24439

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