Multi-Branch Cooperation Networks for Enhanced Click-Through Rate Prediction in Large-Scale E-Commerce Search

Xu Chen, Zida Cheng, Shuai Xiao, Chen Ju, Xiaoming Liu, Jinsong Lan, Xiaoyong Zhu, Bo Zheng

Keywords: multi-branch cooperation, CTR prediction, feature grouping, branch co-teaching, moderate differentiation

Abstract:

Existing Click-Through Rate (CTR) prediction models use various feature interaction techniques, each with unique strengths, but relying on a single type limits their ability to capture complex relationships. 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 Extensible Feature Grouping and Crossing (EFGC) branch that promotes the model's memorization ability of specific feature combinations, the low rank Cross Net branch and Deep branch to enhance explicit and implicit feature crossing for generalization. Among them, a novel cooperation scheme is proposed based on two formulated objectives: branch co-teaching that encourages well-learned branches to support poorly-learned ones on specific training samples, and moderate differentiation that advocates branches to maintain a reasonable level of difference in their feature representations on the same inputs. This cooperation strategy improves learning through mutual knowledge sharing 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.

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Submission Number: 12