Designing a Recommendation Engine

Recommendation engines personalize content to increase engagement and sales.

Types of Recommendations

Collaborative Filtering

Recommend based on similar users' behavior.

User A liked: [Item1, Item2, Item3]
User B liked: [Item1, Item2, Item4]
→ Recommend Item4 to User A

Types:

• User-based: Find similar users
• Item-based: Find similar items (more scalable)

Content-Based Filtering

Recommend items similar to what user liked.

User liked: Action movies
→ Recommend more Action movies

Matrix Factorization (ALS)

Decompose user-item matrix into latent factors.

Ratings Matrix (Users × Items)
→ User factors matrix + Item factors matrix
→ Predicted rating = user_vec · item_vec

System Architecture

User Events → Kafka → Feature Store
                           ↓
                    ML Training (Spark)
                           ↓
                    Model Store
                           ↓
              Inference Service (real-time)
                           ↓
                    API → User

Online vs Offline

• Offline: Train models on historical data (daily/weekly)
• Online: Real-time feature updates (clicks, views)
• Near-real-time: Batch scores updated hourly

A/B Testing

Always test recommendation strategies:

• Control: Popularity-based
• Treatment: ML-personalized
• Measure: CTR, watch time, conversion

Conclusion

Item-based collaborative filtering + matrix factorization is the industry standard. Real-time features + daily model retraining keeps recommendations fresh.