OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation

2026-06-15

AI Summary

This work addresses key limitations in conventional multi-task recommendation systems, where the decoupling of the Transformer encoder and task-specific prediction heads often leads to information bottlenecks, gradient interference, and misalignment between learned representations and prediction dynamics. To overcome these issues, we propose OneRank, the first end-to-end native multi-task ranking Transformer architecture that intrinsically embeds multi-task inference within the model. OneRank employs task-private channels for task-specific modeling, integrates task-conditional feature selection, candidate-aware contextualization, and dynamic matching scoring mechanisms, and introduces cross-task gradient decoupling to mitigate interference. Extensive offline and online experiments on large-scale industrial datasets demonstrate that OneRank significantly outperforms state-of-the-art baselines while maintaining high computational efficiency.

Abstract

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.

Problem

• multi-task learning
• Transformer
• recommender systems
• gradient interference
• information bottleneck

Innovation

• Transformer-native
• multi-task recommendation
• task-private channels
• gradient detachment
• dynamic matching scoring