Multi-scale temporal processing system with culinary context embeddings
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.
1 . A computer system comprising a hardware memory, wherein the computer system is configured to execute software instructions stored on nontransitory machine-readable storage media that cause the computer system to:
operate a temporal embedding generator to:
receive dining input data comprising immediate dining context data and historical dining context data; and
generate temporal embeddings for from the dining input data;
operate a temporal processing engine comprising a short-term processor, a medium-term processor, and a long-term processor to:
process the dining input data using the temporal embeddings at multiple time scales simultaneously to generate temporal outputs based on the temporal embeddings, wherein:
the short-term processor analyzes real-time events and generates temporal outputs for real-time dining behaviors;
the medium-term processor analyzes data over recent timeframes and generates temporal outputs for evolving trends in dining behavior; and
the long-term processor analyzes extended historical data and generates temporal outputs for persistent patterns in dining behavior;
operate a context integration engine to:
receive the dining input data with the temporal embeddings;
extract dining context features from the dining input data, the dining context features comprising domain, entity, and interaction-specific contextual information about dining behavior;
generate dining context representations for different dining context types including individual diner preferences and group dining dynamics; and
operate a fusion engine to:
generate a unified dining representation by integrating the temporal outputs from the temporal processing engine with the dining context representations from the context integration engine by aligning the real-time dining behaviors, evolving trends in dining behavior, and persistent patterns in dining behavior with the domain, entity, and interaction-specific contextual information based on the temporal embeddings; and
generate dining recommendations based on the unified dining representation.
2 . The computer system of claim 1 , wherein the computer system is further configured to:
utilize culinary embeddings comprising both hierarchical ingredient embeddings and dish embeddings to generate the dining context representations.
3 . The computer system of claim 2 , wherein the culinary embeddings encode both cuisine-specific preparation methods and cultural significance metadata to inform the dining recommendations.
4 . The computer system of claim 1 , wherein the computer system is further configured to:
process both individual diner and group dining preferences and dynamics through parallel attention mechanisms to generate the dining context representations.
5 . The computer system of claim 1 , wherein the computer system is further configured to:
dynamically adjust weights between temporal embeddings and culinary embeddings based on current dining session progression.
6 . The computer system of claim 1 , wherein temporal coherence of the unified dining representation is maintained by tracking both current dining session states and historical preference evolution states across the multiple time scales.
7 . The computer system of claim 2 , wherein the dining recommendations are generated using parallel transformer networks that simultaneously process the temporal embeddings and culinary embeddings while maintaining context coherence.
8 . A computer-implemented method comprising the steps of:
using a temporal embedding generator operating on a computer system to perform the steps of:
receiving dining input data comprising immediate dining context data and historical dining context data;
generating temporal embeddings from the dining input data; and
using a temporal processing engine comprising a short-term processor, a medium-term processor, and a long-term processor operating on the computer system to perform the steps of:
processing the dining input data using the temporal embeddings at multiple time scales simultaneously to generate temporal outputs based on the temporal embeddings, wherein:
the short-term processor analyzes real-time events and generates temporal outputs for real-time dining behaviors;
the medium-term processor analyzes data over recent timeframes and generates temporal outputs for evolving trends in dining behavior; and
the long-term processor analyzes extended historical data and generates temporal outputs for persistent patterns in dining behavior;
using a context integration engine operating on the computer system to perform the steps of:
receiving the dining input data with the temporal embeddings;
extracting dining context features from the dining input data, the dining context features comprising domain, entity, and interaction-specific contextual information about dining behavior;
generating dining context representations for different dining context types including individual diner preferences and group dining dynamics; and
using a fusion engine operating on the computer system to perform the steps of:
generating a unified dining representation by integrating the temporal outputs from the temporal processing engine with the dining context representations from the context integration engine by aligning the real-time dining behaviors, evolving trends in dining behavior, and persistent patterns in dining behavior with the domain, entity, and interaction-specific contextual information based on the temporal embeddings; and
generating dining recommendations based on the unified dining representation.
9 . The method of claim 8 , further comprising the step of utilizing culinary embeddings comprising both hierarchical ingredient embeddings and dish embeddings to generate the dining context representations.
10 . The method of claim 9 , wherein the culinary embeddings encode both cuisine-specific preparation methods and cultural significance metadata to inform the dining recommendations.
11 . The method of claim 8 , further comprising the step of processing both individual diner and group dining preferences and dynamics through parallel attention mechanisms to generate the dining context representations.
12 . The method of claim 8 , further comprising the steps of dynamically adjusting weights between temporal embeddings and culinary embeddings based on current dining session progression.
13 . The method of claim 8 , wherein temporal coherence of the unified dining representation is maintained by tracking both current dining session states and historical preference evolution states across the multiple time scales.
14 . The method of claim 9 , wherein the dining recommendations are generated using parallel transformer networks that simultaneously process the temporal embeddings and culinary embeddings while maintaining context coherence.