OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation
Summary
OneRank proposes a Transformer-native multi-task ranking framework that integrates feature encoding and prediction to reduce inter-task interference and improve ranking performance in recommender systems.
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Paper page - OneRank: Unified Transformer-Native Ranking Architecture for Multi-Task Recommendation
Source: https://huggingface.co/papers/2606.16838 Authors:
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Abstract
OneRank presents a Transformer-native multi-task learning framework that integrates feature encoding and prediction to reduce inter-task interference and improve ranking performance in recommender systems.
Multi-task learning(MTL) is essential inrecommender systemsto enable complementary learning among diverse user feedback. While modern industrial practices have shifted from DNNs toTransformer-centric architectures to strengthen sequence modeling and scaling capacity, they still decouple feature encoding from multi-task prediction, treating theTransformeras 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, aTransformer-native multi-task ranking framework that eliminates encoder-predictor separation and introducestask-private channelsfor 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 throughtask-conditioned information selection,candidate-aware contextualization, and controlledcross-task interaction. In the backward pass, cross-taskgradient detachmentisolates task-private parameter updates from shared knowledge extraction modules, preventingnegative transfer. We further replace static task-specific MLP scorers withdynamic matching-based scoringfor context-aware personalized ranking. By internalizing multi-task reasoning within theTransformerstack, 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.
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