How do you scale recommendations for millions of users?

To scale recommendation systems for millions of users, you need to address three core challenges: handling large datasets, processing requests efficiently, and optimizing algorithms for performance. The goal is to balance accuracy with computational efficiency while ensuring the system remains responsive under heavy load.

First, focus on data management and infrastructure. Storing and accessing user and item data requires distributed systems like Apache Cassandra or Amazon DynamoDB, which handle high read/write throughput and partition data across servers. For example, sharding user profiles by region or user ID spreads the load, preventing bottlenecks. Caching frequently accessed data (e.g., popular items or active user preferences) using tools like Redis reduces database hits. Precompute user-item affinity scores offline using batch processing frameworks like Apache Spark, which allows periodic updates without real-time computation. Content delivery networks (CDNs) can also cache static recommendation results for users in specific geographic regions, reducing latency.

Next, optimize processing pipelines. Real-time recommendations require low-latency systems, so separate batch and real-time workflows. Use streaming frameworks like Apache Flink or Kafka Streams to process clickstream data and update user preferences on the fly. For instance, if a user adds an item to their cart, the system can immediately adjust recommendations using real-time events. Load balancing and horizontal scaling (e.g., Kubernetes auto-scaling) ensure servers handle traffic spikes. Asynchronous processing for non-critical tasks, like logging or secondary recommendations, prevents blocking primary request flows. A/B testing frameworks can help validate performance without disrupting live systems.

Finally, simplify recommendation algorithms. Collaborative filtering or matrix factorization may work for small datasets but become impractical at scale. Switch to approximate methods like nearest-neighbor search using libraries like FAISS or Annoy, which find similar items in milliseconds. Embedding-based models (e.g., Word2Vec for items) reduce dimensionality, making comparisons faster. For example, instead of calculating similarities across all users, group users into clusters and compare within those clusters. Lightweight machine learning models, such as logistic regression or shallow neural networks, can replace complex deep learning models if they maintain acceptable accuracy. Regularly prune inactive users or stale items to keep datasets manageable.