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

• URL: http://arxiv.org/abs/2607.24439v1
• Date: Mon, 27 Jul 2026 13:47:38 GMT
• Title: Unifying Generative Recall and Multi-Objective Ranking in a Single Decoder-Only Sequence
• Abstract summary: generative recall and ranking scaling share a common Transformer-based modeling philosophy.<n>We propose textbfUniR$2$, a textbfUnified decoder-only Transformer that unifies Generative textbfRecall and Multi-Objective textbfRanking.
• Score: 26.231002784432842
• License: http://creativecommons.org/licenses/by/4.0/
• 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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