AI Personalization Engine Builder Pro

Comprehensive Personalization Engine for Sustainable Fashion E-Commerce Platform

Phase 1: Algorithm Selection Based on Data Structure

User Data Available:

Purchase history
Browsing behavior
Demographics
Wishlist items
Size preferences

Content Metadata:

Product categories
Brand names
Sustainability ratings
Price ranges
Seasonal tags

Recommended Algorithms:

Hybrid Collaborative Filtering + Content-Based Filtering


Model-based (Matrix Factorization using ALS or LightFM)
Incorporate side information (e.g. size, sustainability tags)


Deep Learning Approaches


Wide & Deep networks (TensorFlow)
Two-tower neural networks (separate user/content encoders)


Reinforcement Learning (Bandit models)


For real-time session-based optimization

Cold Start Solutions:

For new users: style quiz, demographic-based fallback
For new products: content-based embeddings using metadata


Phase 2: Dynamic User Profiling System

Explicit Signals:

Style quizzes
Wishlist items
Feedback forms

Implicit Signals:

Page views
Time on product
Add to cart
Searches
Click patterns

User Profile Schema:
{
  "user_id": "12345",
  "demographics": {"age": 29, "gender": "female", "location": "Berlin"},
  "style_tags": ["boho", "minimalist"],
  "preferred_sizes": ["M", "L"],
  "wishlist": ["product_23", "product_56"],
  "interaction_history": {
    "viewed": [...],
    "clicked": [...],
    "added_to_cart": [...],
    "purchased": [...]
  }
}

Signal Weighting & Update Frequency:

Use a recency-weighted decay function (exponential) for implicit signals
Explicit preferences overwrite older implicit ones
Daily updates via scheduled jobs or microservices


Phase 3: Content Scoring & Ranking System

Factors:

Match to user profile
Inventory level
Profit margin
Seasonal relevance
Content freshness
Trending items (global + cohort-based)

Scoring Formula (sample simplified):
score = (
    user_match_score * 0.4 +
    trendiness_score * 0.2 +
    inventory_factor * 0.1 +
    profit_margin_score * 0.1 +
    freshness_score * 0.1 +
    seasonal_score * 0.1
)

Tech Stack for Fast Scoring:

Real-time: Redis for caching top-N items
Batch: Apache Spark or Dask on AWS EC2
Ranking: Faiss or Annoy for vector-based nearest neighbors


Phase 4: Real-Time Personalization System

Touchpoints:

Homepage
Search
Product detail pages
Email campaigns

Architecture Overview:
            [User Behavior Events]           
                    ↓
[Event Stream (Kafka/Kinesis)] --→ [Feature Store (Redis/Aurora)]
                    ↓
           [Real-time Engine API (FastAPI)]
                    ↓
         [Ranking Service + Caching Layer]
                    ↓
             [Personalized Frontend UI]

Session-Level Adaptation:

Lightweight RL-based models (multi-armed bandits)
Immediate feature updates to Redis and vector encoders

Concurrency Planning:

15,000 concurrent users: use AWS Auto Scaling + ALB + Redis Cluster + ECS


Phase 5: A/B Testing Framework

Test Variables:

Algorithm type
Placement (homepage, product page)
Count of recommendations
Personalization timing (on load, delayed)

Architecture:

Use feature flag system (e.g., LaunchDarkly or open-source like Unleash)
User group bucketing via hashing user_id
Store variant assignment in PostgreSQL + cookie

Measurement Plan:

Split by cohorts (e.g., frequent vs. new users)
Track conversions, CTR, session length per variant
Use CUPED or Bayesian A/B testing for small samples


Phase 6: Evaluation & Monitoring

KPIs:

CTR (click-through rate)
Conversion rate
Revenue per user
Cart abandonment rate
Time to first purchase

Monitoring Systems:

Grafana + Prometheus (real-time tracking)
Drift detection via Kolmogorov–Smirnov test
Alerting when CTR drops below thresholds


Phase 7: Technical Implementation Details

Existing Stack: Python, Django, PostgreSQL, AWS, Redis, Elasticsearch
Recommendations:

User Embeddings:


Use Faiss with Redis to serve nearest-neighbor queries


Data Pipeline:


Use Apache Airflow (on ECS) for daily profile updates
S3 as data lake for interaction logs


Scalability:


Stateless APIs using FastAPI + Redis cache
Horizontal scaling via ECS/Fargate


Integration:


Inventory sync via Django ORM scheduled tasks
Elasticsearch for fast filtering + faceted search

Budget Considerations:

Prefer open-source models (e.g., LightFM, Faiss)
Leverage AWS free-tier where possible
Use spot instances for training workloads

Privacy & Compliance:

GDPR: include user consent management (CookieBot)
Anonymize data before storage
Provide opt-out and data deletion APIs


Roadmap & Timeline (12 Weeks)

Weeks 1-2: Data audit, finalize architecture, user profile schema
Weeks 3-4: Build MVP profiling + batch recommendation engine
Weeks 5-6: Add real-time session adaptation + Redis caching
Weeks 7-8: Implement A/B testing system, collect early metrics
Weeks 9-10: Integrate across touchpoints (homepage, email)
Weeks 11-12: Launch full system, monitor, iterate

Next Steps:

Define data schemas and user personas
Choose open-source models to prototype
Setup infrastructure (Redis, Faiss, FastAPI)
Build out MVP recommendation engine

Let me know if you want visual architecture diagrams or specific code snippets for implementation.