ByteDance's HELIX Unifies Sequence Retrieval and Feature Interaction, Deployed in TikTok

_reachsumit · x · 2026-09-30

ByteDance published HELIX, a new ranker architecture for large-scale recommendation, already deployed in TikTok's e-commerce system.

• Key finding: Scaling feature interaction or long sequence modeling in isolation hits limited ceilings with suboptimal scaling-law slopes; joint scaling of both axes is needed for a better slope.
• Design: HELIX interleaves sequence retrieval and feature interaction, enforcing one-way information flow from reusable sequence states to candidate-conditioned mix-tokens — preserving cross-depth communication while keeping user-side compute amortizable and enabling asymmetric scaling.
• Results: Consistent offline improvements in CTR AUC, CVR AUC, and other ranking metrics in TikTok's e-commerce recommendation.

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