Deep Situation-Aware Interaction Network for Click-Through Rate Prediction

2026-04-14

AI Summary

This work addresses the limitation of existing click-through rate (CTR) prediction methods, which largely overlook the rich contextual information—such as interaction type, timestamp, and location—embedded in user behavior sequences. To remedy this, the authors propose a novel contextual modeling framework that integrates multidimensional contextual features into user behavior representations for the first time. The framework employs reparameterized denoising, contextual feature embedding, triadic interaction fusion, and heterogeneous context aggregation to construct a deep context-aware interaction network. Evaluated on three real-world datasets, the proposed method significantly outperforms state-of-the-art baselines. Online A/B tests demonstrate consistent improvements, with a 2.70% increase in CTR, a 2.62% gain in CPM, and a 2.16% uplift in GMV, leading to its deployment on Meituan Waimai’s primary traffic platform.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70\%, the CPM by 2.62\%, and the GMV by 2.16\% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app.

Problem

• Click-Through Rate Prediction
• User Behavior Sequence
• Situational Features
• Interaction Information
• E-commerce

Innovation

• situation-aware
• reparameterization
• tri-directional correlation fusion
• heterogeneous situation aggregation
• CTR prediction