Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction

TL;DR

This paper introduces a bipartite neural interaction framework that learns both universal and segmentation-specific user representations, improving prediction accuracy in online marketing applications by addressing user distribution differences.

Contribution

The paper proposes a novel bipartite neural interaction approach to jointly learn universal and segmentation-specific user representations, enhancing model robustness and performance.

Findings

Outperforms baseline methods on open-source benchmarks.

Achieves superior CVR prediction in online marketing applications.

Demonstrates robustness across diverse user segments.

Abstract

Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal user representation as the input for subsequent scenario-specific models. However, in numerous industrial applications (e.g., recommendation and marketing), the business always operates such applications as various online activities among different user segmentation. These segmentation are always created by domain experts. Due to the difference in user distribution (i.e., user segmentation) and business objectives in subsequent tasks, learning solely on universal representation may lead to detrimental effects on both model performance and robustness. In this paper, we propose a novel learning framework that can first learn general universal user representation through information bottleneck. Then, merge and learn a segmentation-specific or a task-specific representation through neural interaction. We design the interactive learning process by leveraging a bipartite graph architecture to model the representation learning and merging between contextual clusters and each user segmentation. Our proposed method is evaluated in two open-source benchmarks, two offline business datasets, and deployed on two online marketing applications to predict users' CVR. The results demonstrate that our method can achieve superior performance and surpass the baseline methods.