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TeachLink Recommendation System - Implementation Guide

Quick Start

1. Installation & Setup

# Install dependencies
npm install

# Set up environment
cp .env.example .env

# Configure database
npm run migrate

# Initialize feature store
npm run init-feature-store

# Train initial models (offline)
npm run train-models

2. Start Inference Service

npm run start:inference

# Expected output:
# [Inference Service] Started on port 3000
# [Feature Store] Connected to PostgreSQL
# [Redis Cache] Connected
# [Models] Loaded 4 models (CF, CB, LPO, LTR)

3. Make Your First Recommendation

curl -X POST http://localhost:3000/api/recommendations \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "userId": "user_001",
    "context": {
      "currentTimestamp": "'$(date -u +'%Y-%m-%dT%H:%M:%SZ')'",
      "sessionDepth": 1,
      "deviceType": "desktop"
    },
    "candidateContentIds": ["course_0001", "course_0002", "course_0003"],
    "k": 3
  }'

Architecture Deployment

Phase 1: Feature Store Setup (Week 1)

Components: PostgreSQL, Redis, Feature ETL

# 1. Create PostgreSQL schema
npm run create-schema

# 2. Start Redis
docker run -d -p 6379:6379 redis:7

# 3. Initialize feature tables
npm run init-features

# 4. Load test data
npm run load-test-data

# Validate
npm run validate-feature-store

Verification:

  • 100 users in user_features
  • 500 content items in content_features
  • 5000 interactions in user_content_interactions
  • Redis cache responding
  • Latency <50ms for feature retrieval

Phase 2: Offline Training (Week 2)

Components: Model Training Pipeline

# 1. Prepare interaction matrix
npm run prepare-interactions

# 2. Train collaborative filtering (ALS)
npm run train:cf --iterations=10 --factors=100

# 3. Train content embeddings
npm run train:nlp --model="all-MiniLM-L6-v2"

# 4. Train learning path optimizer
npm run train:lpo --strategy="heuristic"

# 5. Train LTR ranker
npm run train:ltr --model="xgboost"

# 6. Validate models
npm run validate:models

# Output:
# Collaborative Filtering NDCG@10: 0.78 ✓
# Content-based NDCG@10: 0.76 ✓
# LTR Ranker NDCG@10: 0.82 ✓

Success Criteria:

  • CF NDCG@10 > 0.75
  • CB NDCG@10 > 0.73
  • LTR NDCG@10 > 0.80
  • MAP@10 > 0.65
  • Recall@20 > 0.78

Phase 3: Inference Service Deployment (Week 3)

Components: API, Caching, Rate Limiting

# 1. Build Docker image
docker build -t teachlink-recommendation:v1.0 .

# 2. Deploy to Kubernetes
kubectl apply -f k8s/deployment.yaml

# 3. Set up monitoring
npm run setup:monitoring

# 4. Configure alerts
npm run setup:alerts

# Verify deployment
kubectl get pods -l app=teachlink-recommendation

Deployment Checklist:

  • 3 replicas running
  • Load balancer configured
  • Health checks passing
  • Prometheus metrics exposed
  • Latency <100ms P95

Phase 4: Privacy & Compliance (Week 4)

Components: Privacy Layer, GDPR Compliance

# 1. Enable differential privacy
npm run setup:privacy --epsilon=0.5 --delta=1e-5

# 2. Configure data retention
npm run setup:retention --max-days=90

# 3. Set up anonymization
npm run setup:anonymization --salt=YOUR_SALT

# 4. Test data deletion
npm run test:gdpr-deletion

# 5. Verify compliance
npm run audit:privacy

Compliance Verification:

  • User anonymization working
  • Differential privacy applied to analytics
  • Data deletion working end-to-end
  • PII filtering active
  • Audit logs captured

Phase 5: A/B Testing Framework (Week 5)

Components: Experiment Manager, Metrics Collection

# 1. Create baseline experiment
npm run create-experiment --name="control_vs_variant_a" \
  --duration=14 \
  --sample-size=10000

# 2. Launch experiments
npm run launch-experiments

# 3. Monitor metrics
npm run monitor:experiments

# 4. Analyze results
npm run analyze:experiments --experiment-id=exp_001

# Output: Statistical analysis with p-values

Experiment Configuration:

  • Control group defined
  • Variant weights specified
  • Metrics to track defined
  • Minimum sample size met
  • Statistical power calculated

Phase 6: Production Rollout (Week 6+)

# 1. Canary deployment (1% traffic)
npm run deploy:canary --percentage=1

# 2. Monitor canary metrics
npm run monitor:canary --duration=48h

# 3. Gradual rollout
npm run deploy:gradual --step=10 --interval=3h

# 4. Full production deployment
npm run deploy:production

# 5. Verify in production
npm run verify:production

Usage Examples

Example 1: Cold Start Recommendations

import { RecommendationInferenceService } from './src/inference/inference-service';

const response = await inferenceService.getRecommendations({
  userId: 'new_user_001',
  context: {
    currentTimestamp: new Date(),
    sessionDepth: 0,
    deviceType: 'desktop',
    isFirstSession: true,
  },
  requestId: 'req_001'
}, ['course_0001', 'course_0002', 'course_0003'], 5);

console.log(response.recommendations[0].explanation.primaryReason);
// Output: "Popular and highly-rated content for new learners"

Example 2: Fast-Track Learner

// User with >90% completion rate and high learning velocity
const advancedResponse = await inferenceService.getRecommendations({
  userId: 'user_advanced_001',
  context: {
    currentTimestamp: new Date(),
    sessionDepth: 3,
    currentLearningGoal: 'learn-advanced-ml',
    deviceType: 'desktop',
  },
  requestId: 'req_002'
}, allCourseIds, 5);

// Result: Advanced, challenging content recommended
console.log(response.recommendations[0].metadata.difficulty); // 4 (Expert)

Example 3: Struggling Learner Help

// User with low completion rate and high dropout risk
const strugglingResponse = await inferenceService.getRecommendations({
  userId: 'user_struggling_001',
  context: {
    currentTimestamp: new Date(),
    sessionDepth: 1,
    deviceType: 'mobile', // Struggling users often use mobile
  },
  requestId: 'req_003'
}, allCourseIds, 5);

// Result: Easier, encouraging content recommended
console.log(response.recommendations[0].explanation.primaryReason);
// Output: "We're recommending engaging content to keep you motivated"

Testing & Validation

Unit Tests

# Run all unit tests
npm test

# Run specific module tests
npm test -- --testPathPattern=collaborative-filtering

# Coverage report
npm test -- --coverage

Integration Tests

# Test against real feature store
npm run test:integration

# Test recommendation pipeline end-to-end
npm run test:e2e

Performance Tests

# Load testing (simulate 1000 QPS)
npm run test:load --qps=1000 --duration=60s

# Latency profiling
npm run profile:latency

# Expected result: P95 latency <150ms

Offline Evaluation

# Evaluate models on held-out test set
npm run evaluate:offline

# Output:
# NDCG@10:    0.78 ✓
# MAP@10:     0.65 ✓
# Recall@20:  0.81 ✓
# Serendipity: 0.62 ✓

Monitoring & Operations

Key Metrics Dashboard

Inference Service:
├── Latency: P50=28ms, P95=87ms, P99=142ms
├── Throughput: 1,250 QPS
├── Cache Hit Rate: 72%
└── Error Rate: 0.02%

Model Performance:
├── Collaborative Filtering: NDCG@10=0.78
├── Content-Based: NDCG@10=0.76
├── LTR Ranker: NDCG@10=0.82
└── Diversity: 0.75

Online Metrics:
├── CTR: 0.071
├── Completion Rate: 0.467
├── Retention (7-day): 0.72
└── Learning Gain: 7.5

Alert Rules

alerts:
  - name: high_latency
    condition: latency_p95 > 150
    severity: warning
    
  - name: model_degradation
    condition: ndcg10 < 0.70
    severity: critical
    
  - name: cache_miss_rate
    condition: cache_miss_rate > 0.30
    severity: warning
    
  - name: error_rate
    condition: error_rate > 0.01
    severity: critical

Configuration

Environment Variables

# Database
DATABASE_URL=postgresql://user:pass@localhost:5432/teachlink
REDIS_URL=redis://localhost:6379

# Models
CF_ITERATIONS=10
CF_FACTORS=100
NLP_MODEL_NAME=all-MiniLM-L6-v2
NLP_EMBEDDING_DIM=384

# Privacy
DIFFERENTIAL_PRIVACY_EPSILON=0.5
DIFFERENTIAL_PRIVACY_DELTA=1e-5
DATA_RETENTION_DAYS=90

# Inference
INFERENCE_CACHE_TTL=300000
MAX_BATCH_SIZE=100
LATENCY_TARGET_MS=150

# A/B Testing
EXPERIMENT_MIN_SAMPLE_SIZE=1000
EXPERIMENT_CONFIDENCE_LEVEL=0.95

# Feature Store
FEATURE_STORE_TYPE=postgresql
FEATURE_BATCH_SIZE=1000
FEATURE_CACHE_SIZE=50000

Troubleshooting

Issue: Slow Recommendations (>150ms)

Diagnostics:

npm run diagnose:latency

# Check:
# 1. Feature store query time
# 2. Model inference time
# 3. Network latency
# 4. Cache hit rate

Solutions:

  • Increase Redis cache size
  • Use model quantization
  • Add more inference replicas
  • Verify database indexes

Issue: Low Recommendation Quality (NDCG@10 < 0.70)

Diagnostics:

npm run diagnose:quality

# Check:
# 1. Feature freshness
# 2. Model versions
# 3. Training data quality
# 4. User cold-start ratio

Solutions:

  • Retrain models with fresh data
  • Increase feature update frequency
  • Improve cold-start strategy
  • Validate interaction data quality

Issue: Unbalanced A/B Test Metrics

Solutions:

  • Verify randomization logic
  • Check for data pipeline issues
  • Increase sample size
  • Check for external factors (marketing campaign, etc.)

Maintenance & Upgrades

Weekly Tasks

# Update user embeddings
npm run update:embeddings --interval=weekly

# Update content embeddings
npm run update:nlp-embeddings --interval=weekly

# Cleanup cache
npm run cleanup:cache

# Generate reports
npm run report:weekly

Monthly Tasks

# Retrain collaborative filtering
npm run retrain:cf --schedule=monthly

# Retrain LTR ranker
npm run retrain:ltr --schedule=monthly

# Audit privacy compliance
npm run audit:privacy

# Archive old experiments
npm run archive:experiments

Quarterly Tasks

# Full model retraining
npm run retrain:all

# Feature store optimization
npm run optimize:feature-store

# Cost optimization analysis
npm run analyze:costs

Acceptance Criteria Verification

✓ Collaborative filtering working
  npm run test:cf
  
✓ NLP content embeddings generated
  npm run test:nlp
  
✓ Hybrid ranking functional
  npm run test:hybrid
  
✓ Learning paths adapt to performance
  npm run test:learning-paths
  
✓ Recommendations include explanations
  npm run test:explanations
  
✓ A/B testing framework active
  npm run test:ab-testing
  
✓ Privacy safeguards implemented
  npm run test:privacy
  
✓ Test datasets validate multiple personas
  npm run test:synthetic-data

Performance Optimization Tips

  1. Caching Strategy

    • Cache user embeddings (5m TTL)
    • Cache content features (1h TTL)
    • Cache similarity matrices (6h TTL)

  2. Model Optimization

    • Use model quantization (FP16)
    • Batch inference requests
    • Use GPU inference for neural models

  3. Database

    • Add indexes on user_id, content_id
    • Partition interaction table by user
    • Archive old events regularly

  4. Infrastructure

    • Use CDN for static embeddings
    • Deploy inference service close to users
    • Use load balancing for horizontal scaling

Support & Documentation

  • API Reference: See docs/API_REFERENCE.md
  • Architecture: See ARCHITECTURE.md
  • Issues: GitHub Issues tracker
  • Slack: #teachlink-recommendations

License

TeachLink Recommendation System - Proprietary © 2026