Recommendation Systems at Scale

Recommendation systems power Netflix, Amazon, Spotify, YouTube - driving 30-50% engagement increases and 20-40% revenue growth. Modern systems combine collaborative filtering, content-based filtering, and deep learning for personalized experiences.

Core Approaches

1. Collaborative Filtering

User-Based CF: "Users similar to you liked..."

• Find users with similar preferences
• Recommend items they liked
• Works well for mature systems with many users

Item-Based CF: "Customers who bought this also bought..."

• Find similar items based on user interactions
• More stable than user-based (items change less than users)
• Powers Amazon's recommendations

Matrix Factorization:

• Decompose user-item matrix into latent factors
• SVD, ALS (Alternating Least Squares)
• Handles sparse data well
• Netflix Prize winner approach

2. Content-Based Filtering

• Recommend based on item features (genre, category, attributes)
• User profile from past interactions
• TF-IDF, embeddings for text content
• Solves cold-start problem for new users

3. Deep Learning Approaches

Neural Collaborative Filtering:

• Replace dot product with neural network
• Learn complex non-linear patterns
• 10-20% improvement over traditional CF

Two-Tower Models:

• Separate encoders for users and items
• Efficient for billion-scale catalogs
• YouTube, Pinterest architecture

Transformers for RecSys:

• Model sequential user behavior
• Capture long-term dependencies
• State-of-the-art results

4. Hybrid Systems

• Combine multiple approaches (CF + content + deep learning)
• Weighted ensemble or stacking
• Best of all worlds
• Industry standard for production systems

Evaluation Metrics

• Offline: RMSE, MAE, Precision@K, Recall@K, NDCG, MAP
• Online: CTR, conversion rate, time on site, revenue per user
• Business: GMV increase, engagement lift, retention improvement

Challenges & Solutions

Cold Start Problem

• New Users: Use content-based, ask preferences, exploit features
• New Items: Use item features, show to exploratory users, hybrid approach

Scalability

• Approximate nearest neighbors (ANN) for fast retrieval
• FAISS, Annoy, ScaNN for billion-scale search
• Batch vs real-time computation tradeoffs

Diversity vs Relevance

• Pure relevance → filter bubble
• Add diversity constraints
• Multi-objective optimization

Implementation Stack

Libraries:

• Surprise, LightFM for traditional CF
• TensorFlow Recommenders, PyTorch for deep learning
• Apache Spark MLlib for distributed computing

Production:

• Redis for caching recommendations
• Kafka for real-time events
• Feature stores (Feast, Tecton)
• A/B testing platforms

Case Study: E-commerce Recommendations

• Scale: 10M users, 100K SKUs
• System: Hybrid (item-based CF + content + neural network)
• Results:
  • CTR: 2.1% → 3.8% (+81%)
  • Conversion rate: 3.2% → 4.5% (+41%)
  • AOV: +18%
  • Revenue: +₹15Cr/year from recommendations
  • Engagement: +42% time on site

Pricing

• Basic System: ₹15-30L (collaborative filtering)
• Advanced: ₹40-80L (hybrid, deep learning)
• Enterprise: ₹80L-3Cr (real-time, billion-scale)