AI 中文总结
CCFormer是腾讯提出的高效Transformer骨干网络，通过跨域交互与分层序列压缩技术，在工业推荐场景中取得显著性能提升，已部署于腾讯生产系统服务核心流量。
AI 中文摘要
近期工业级推荐系统研究表明，基于自注意力的序列推荐模型可通过增加序列长度和模型容量从可预测缩放定律中获益。然而，实际推荐系统存在严格的延迟与资源约束，使得平衡计算开销与细粒度特征交互颇具挑战。本文提出CCFormer，一种统一跨域特征交互与压缩长序列建模的高效Transformer骨干网络，用于工业级推荐。具体而言，CCFormer将特征域分离的交叉注意力与长序列子空间令牌混合相结合，以挖掘异构特征域间的长期偏好信号；采用感受野逐步扩展的分层序列压缩策略，可在减少信息损失的同时实现高效长序列建模。在两个公开基准数据集及大规模工业数据集上的大量实验表明，CCFormer始终优于现有最优基线。腾讯视频推荐场景与广告排序场景的在线A/B测试进一步验证了其工业实用性，分别取得3.57%的点击率（CTR）提升与1.71%的广告收入提升，同时相比强劲基线HSTU将模型训练速度加快2.21倍。目前CCFormer已全面部署于腾讯生产环境推荐系统，服务于上述两个场景的主要流量。
英文摘要
Recent studies in industrial recommendation systems have demonstrated that sequential recommendation models built upon self-attention can benefit from predictable scaling laws by increasing sequence length and model capacity. However, practical recommender systems impose strict latency and resource constraints, making it challenging to balance computational overhead with fine-grained feature interaction. In this paper, we propose CCFormer, an efficient Transformer backbone that unifies cross-field feature interaction and compressed long-sequence modeling for industrial recommendation. Specifically, CCFormer combines feature-field separated cross attention with long-sequence subspace token mixing to exploit long-term preference signals across heterogeneous feature domains. A hierarchical sequence compression strategy with progressively expanded receptive fields enables efficient long-sequence modeling with reduced information loss. Extensive experiments on two public benchmarks and a large-scale industrial dataset demonstrate that CCFormer consistently outperforms state-of-the-art baselines. Online A/B tests in a video recommendation scenario and an advertising ranking scenario at Tencent further validate its industrial practicality, yielding a 3.57% CTR gain and a 1.71% advertising revenue lift, respectively, while accelerating model training by 2.21x over the strong HSTU baseline. CCFormer has been fully deployed in Tencent's production recommendation system, serving the main traffic of both scenarios.
