Tencent Proposes CCFormer: Efficient Long-Sequence Modeling for Industrial Recommenders
_reachsumit · x · 2026-07-31
Tencent's research team proposed CCFormer, an efficient Transformer architecture designed to tackle the computational latency and resource bottlenecks of self-attention in industrial recommendation systems.
Core Mechanisms:
- Feature-field separated cross attention: Handles fine-grained interactions across heterogeneous feature domains.
- Hierarchical sequence compression: Reduces information loss in long-sequence modeling by progressively expanding receptive fields.
Experiments demonstrate that CCFormer consistently outperforms state-of-the-art baselines on public and large-scale industrial datasets. Its industrial value is further validated through online A/B tests in Tencent's video recommendation and advertising ranking scenarios.
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