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Yu-Fei Feng

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Preprint Sep 2026

HELIX: Purified and Unified - Rethinking Feature Interaction and Sequence Modeling for Large-Scale Recommendation

Industrial recommendation ranking models typically scale along two modeling axes: feature interaction over heterogeneous user, item, context, and cross features, and sequence modeling over long, informative, and multi-type user behavior histories. We find that scaling either capability in isolation is insufficient, as...

Yun-Tao Zheng, Miao Zhang, Ya-Dong Ding et al. · 0 citations
Preprint Sep 2026

OneTrans-V2: Unifying Retrieval, Pre-rank, and Fine-rank with One Transformer in Industrial Recommender

Industrial recommendation systems typically operate as a \emph{cascade} of retrieval, pre-rank, and fine-rank, but these stages are usually trained and served as separate models, causing repeated user-sequence encoding, isolated optimization, and duplicated engineering effort. Building on OneTrans'model-level unificati...

Han-Nan Cao, Jun Guo, Hao-Lei Pei et al. · 0 citations

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