Context-Driven Recommendation via Heterogeneous Temporal Modeling and Large Language Model in the Takeout System

Abstract

On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes.