🍗 Food Recommendation Engine
Advanced AI-Powered Food Discovery & Personalization Platform
18.5%
Strict LOO R@1
16.8%
Temporal R@1
3K
Test Orders
Strict LOO Recall@1
18.53%
↗️
Strong Performance
Temporal Recall@1
16.77%
↗️
Time-aware Evaluation
NDCG@3 Performance
27.61%
↗️
Ranking Quality
MAP@3 Score
25.22%
↗️
Precision Excellence
📊 Cart Size Distribution Analysis
🎯 Model Performance Comparison
ML-Powered Personalization
Advanced neural networks analyzing user behavior, preferences, and contextual signals for hyper-personalized recommendations achieving 18.5% recall rate.
Rigorous Evaluation
Comprehensive evaluation with both strict Leave-One-Out and temporal validation methodologies ensuring robust real-world performance.
Context-Aware Recommendations
Multi-dimensional context integration including channel, store, customer, and occasion signals for superior recommendation relevance.
Continuous Learning
Online learning algorithms that adapt to changing user preferences and seasonal trends, maintaining peak performance over time.
🎯 Recall Performance Analysis
📈 Top Food Items Popularity
🔥 Item Co-occurrence Heatmap
📊 Weekly Order Patterns
📈 Item Popularity Long Tail
🏪 Orders by Channel
🛍️ Orders by Subchannel
🎪 Orders by Occasion
🏪 Top 15 Stores by Orders
📅 Weekly Unique Items
🛒 Weekly Average Cart Size
🔗 Item Co-occurrence Degree
📈 Monthly Orders Trend
⚙️
Technical Specifications
Model Architecture
Multi-Context Neural Collaborative Filtering with attention mechanisms and embedding layers
Evaluation Methodology
Strict Leave-One-Out (3K orders) + Temporal validation with comprehensive ranking metrics
Performance Metrics
Recall@1: 18.53%, Recall@3: 34.57%, MAP@3: 25.22%, NDCG@3: 27.61%
Context Features
Global, Channel, Store, Customer, Occasion, Subchannel contextual embeddings
Training Data
Multi-million order history with rich contextual metadata and user interaction patterns
Deployment Infrastructure
Kubernetes auto-scaling, multi-region redundancy, edge computing optimization
🏗️
System Architecture Flow
1
Data Ingestion
Orders, Context, User Behavior, Real-time Events
2
Feature Engineering
Context Weights, Embeddings, Signal Processing
3
Model Training
Neural Networks, Optimization, Cross-Validation
4
Evaluation
LOO & Temporal Testing, Performance Metrics
5
Deployment
Real-time API, Monitoring, Feedback Loop
📊
Key Performance Indicators
34.57%
Recall@3 (Strict)
33.43%
Recall@3 (Temporal)
25.22%
MAP@3 (Strict)
23.85%
MAP@3 (Temporal)
27.61%
NDCG@3 (Strict)
26.30%
NDCG@3 (Temporal)