CCFormer通过高效跨场交互与分层压缩，提升工业推荐系统性能与效率。

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.