# OneRank：面向多任务推荐的统一Transformer原生排序架构

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-15 08:00
- AIHOT 分数：36
- AIHOT 链接：https://aihot.virxact.com/items/cmqg55i9o0007sl45d6gga0y1
- 原文链接：https://arxiv.org/abs/2606.16838

## AI 摘要

OneRank是一种Transformer原生的多任务排序框架，消除编码器与预测器分离。前向通过任务条件信息选择、候选感知上下文化和受控跨任务交互自底向上学习任务特定表示；后向通过跨任务梯度隔离防止负迁移。采用动态匹配评分替代静态MLP评分器，实现上下文感知排序。大规模工业数据集上的离线和在线实验表明，OneRank优于基线且保持计算效率。

## 正文

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.