Multi-scale temporal processing system with culinary context embeddings

Abstract

A computer system processes dining context data across multiple time scales to generate personalized dining recommendations. The system includes a temporal processing engine that simultaneously analyzes both immediate and historical dining data using multiple time-scale processors while maintaining temporal coherence. A dining context integration engine extracts and organizes features related to individual preferences and group dining dynamics, ensuring state continuity. A fusion engine combines outputs from the temporal processors with contextual representations to generate informed dining recommendations. These components work cooperatively to integrate real-time and historical dining patterns, enhancing dining experiences through adaptive and context-aware recommendations.