AI 中文总结
针对现代工业推荐系统将召回和排序分两阶段进行存在的问题，提出UniR²，在单个异构序列中统一生成式召回与多目标排序，利用双查询前缀因果注意力等方法，经实验验证其在大规模推荐系统中的有效性、效率及实用性。
AI 中文摘要
现代工业推荐系统通常将召回和排序分为两个独立阶段，这会导致目标不一致、候选交接时信息丢失和用户端上下文计算冗余等问题。生成式召回和排序扩展有着共同的基于Transformer的建模理念，具备统一整合的机会。本文提出了UniR²，它是一个统一的仅含解码器的Transformer，在包含用户上下文、SID轨迹和商品特征的单个异构序列中统一了生成式召回和多目标排序。生成轨迹作为召回和排序之间的表示桥梁，双查询前缀因果注意力提供特定任务的可见性。两个任务共享基本注意力权重，但保留单独的优化边界。在大规模工业数据上的大量离线实验证明了UniR²在召回和排序方面的有效性和效率。在快手平台上的长期在线A/B测试进一步显示了一致的积极收益，验证了统一模型在大规模推荐系统中的实用性。
英文摘要
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.
