Existing click-through rate (CTR) prediction works have studied the role of feature interaction through a variety of techniques. Each interaction technique exhibits its own strength, and solely using one type could constrain the model's capability to capture the complex feature relationships, especially for industrial large-scale data with enormous users and items. Recent research shows that effective CTR models often combine an MLP network with a dedicated feature interaction network in a two-parallel structure. However, the interplay and cooperative dynamics between different streams or branches remain under-researched. In this work, we introduce a novel Multi-Branch Cooperation Network (MBCnet) which enables multiple branch networks to collaborate with each other for better complex feature interaction modeling. Specifically, MBCnet consists of three branches: the Expert-based Feature Grouping and Crossing (EFGC) branch that promotes the model's memorization ability of specific feature fields, the low rank Cross Net branch and Deep branch to enhance both explicit and implicit feature crossing for improved generalization. Among branches, a novel cooperation scheme is proposed based on two principles: branch co-teaching and moderate differentiation. Branch co-teaching encourages well-learned branches to support poorly-learned ones on specific training samples. Moderate differentiation advocates branches to maintain a reasonable level of difference in their feature representations. The cooperation strategy improves learning through mutual knowledge sharing via co-teaching and boosts the discovery of diverse feature interactions across branches. Extensive experiments on large-scale industrial datasets and online A/B test demonstrate MBCnet's superior performance, delivering a 0.09 point increase in CTR, 1.49% growth in deals, and 1.62% rise in GMV. Core codes will be released soon.

翻译：现有的点击率预测研究已通过多种技术探究了特征交互的作用。每种交互技术均展现出其独特优势，而仅使用单一类型可能限制模型捕捉复杂特征关系的能力，尤其对于拥有海量用户和商品的工业级大规模数据。近期研究表明，有效的CTR模型常将MLP网络与专用特征交互网络以双并行结构相结合。然而，不同流或分支间的相互作用与协同机制仍未得到充分研究。本文提出一种新颖的多分支协同网络，该网络使多个分支网络能够相互协作以实现更优的复杂特征交互建模。具体而言，MBCnet包含三个分支：基于专家的特征分组与交叉分支，用于增强模型对特定特征域的记忆能力；低秩交叉网络分支与深度网络分支，分别用于增强显式与隐式特征交叉以提升泛化性能。各分支间基于两项原则提出了一种创新的协同机制：分支协同教学与适度差异化。分支协同教学鼓励在特定训练样本上，学习效果良好的分支辅助学习效果欠佳的分支。适度差异化主张各分支在特征表示层面保持合理程度的差异性。该协同策略通过协同教学实现相互知识共享以优化学习过程，并促进跨分支的多样化特征交互发现。基于大规模工业数据集与在线A/B测试的广泛实验表明，MBCnet具有卓越性能，可实现CTR提升0.09个百分点、成交笔数增长1.49%、总交易额提升1.62%。核心代码即将开源。