OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction

TL;DR

OptMSM is a novel framework that enhances multi-scenario CTR prediction by disentangling shared and specific features and using hypernetworks for better scenario-specific predictions, leading to improved efficiency and performance.

Contribution

The paper introduces a simplified scenario-enhanced learning module with orthogonality constraints and a scenario-specific hypernetwork, addressing limitations of existing multi-scenario CTR models.

Findings

Effective disentanglement of shared and scenario-specific features.

Improved prediction accuracy demonstrated in offline experiments.

Positive online A/B test results confirming practical benefits.

Abstract

A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations. However, most of these approaches primarily focus on improving the former module and neglect the latter module. This can result in challenges such as increased model parameter size, training difficulty, and performance bottlenecks for each scenario. To address these issues, we propose a novel framework called OptMSM (\textbf{Opt}imizing \textbf{M}ulti-\textbf{S}cenario \textbf{M}odeling). First, we introduce a simplified yet effective scenario-enhanced learning module to alleviate the aforementioned challenges. Specifically, we partition the input features into scenario-specific and scenario-shared features, which are mapped to specific information embedding encodings and a set of shared information embeddings, respectively. By imposing an orthogonality constraint on the shared information embeddings to facilitate the disentanglement of shared information corresponding to each scenario, we combine them with the specific information embeddings to obtain multi-functional representations. Second, we introduce a scenario-specific hypernetwork in the scenario-specific prediction module to capture interactions within each scenario more effectively, thereby alleviating the performance bottlenecks. Finally, we conduct extensive offline experiments and an online A/B test to demonstrate the effectiveness of OptMSM.