OneRank：面向多任务推荐的统一原生Transformer排序架构

摘要

多任务学习（MTL）在推荐系统中不可或缺，能够促进不同用户反馈之间的互补学习。尽管现代工业实践已从深度神经网络转向以Transformer为核心的架构以增强序列建模与扩展能力，但现有方案仍将特征编码与多任务预测解耦，将Transformer视为任务无关的编码器。这种设计从根本上限制了性能与可扩展性，具体表现为：（1）在异构任务目标下形成信息瓶颈；（2）引发梯度干扰导致跷跷板现象；（3）迫使数据流发生转换——基于注意力机制的上下文自适应表征学习被转化为静态前馈任务预测，且伴随信息读写动态不兼容的问题。我们提出OneRank——一种原生Transformer多任务排序框架，该框架消除了编码器-预测器的分离，引入任务私有通道用于前向表征学习与反向优化，在降低任务间干扰的同时实现任务特化学习。在前向过程中，OneRank通过任务条件信息选择、候选感知上下文化以及受控的跨任务交互，自底向上学习任务特定表征。在反向过程中，跨任务梯度分离将任务私有参数更新与共享知识提取模块隔离，防止负迁移。我们进一步用基于动态匹配的打分机制替代静态的任务特定多层感知机评分器，实现上下文感知的个性化排序。通过将多任务推理内化至Transformer堆栈中，OneRank建立了统一且可扩展的架构范式。在工业级大规模数据集上的离线与在线实验表明，OneRank在保持计算效率的同时显著优于最先进的基线方法。

English

Multi-task learning (MTL) is essential in recommender systems to enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs to Transformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature encoding from multi-task prediction, treating the Transformer as a task-agnostic encoder. This design fundamentally limits the performance and scalability by (1) creating an information bottleneck under heterogeneous task objectives, (2) inducing gradient interference that leads to the seesaw phenomenon, and (3) forcing a dataflow transition in which attention-based, context-adaptive representation learning is converted to static feed-forward task prediction with incompatible information read-write dynamics. We propose OneRank, a Transformer-native multi-task ranking framework that eliminates encoder-predictor separation and introduces task-private channels for forward representation learning and backward optimization, enabling task-specialized learning while reducing inter-task interference. In the forward pass, OneRank learns task-specific representations bottom-up through task-conditioned information selection, candidate-aware contextualization, and controlled cross-task interaction. In the backward pass, cross-task gradient detachment isolates task-private parameter updates from shared knowledge extraction modules, preventing negative transfer. We further replace static task-specific MLP scorers with dynamic matching-based scoring for context-aware personalized ranking. By internalizing multi-task reasoning within the Transformer stack, OneRank establishes a unified and scalable architectural paradigm. Offline and online experiments on large-scale industrial datasets show that OneRank significantly outperforms state-of-the-art baselines while maintaining computational efficiency.