How do you handle large item catalogs in a recommender system?

Handling large item catalogs in recommender systems requires balancing computational efficiency with recommendation quality. The primary challenge is efficiently retrieving relevant items from millions or billions of options without sacrificing real-time performance. A common approach is to use approximate nearest neighbor (ANN) algorithms like FAISS, HNSW, or Annoy, which enable fast similarity searches in high-dimensional spaces. For example, if items are represented as embeddings (dense vectors), ANN indexes allow you to quickly find items similar to a user’s preferences without exhaustively comparing every item. This reduces search time from O(n) to O(log n) or better, making it feasible for large-scale systems.

Another strategy is two-stage retrieval and ranking. In the first stage, a lightweight model or rule-based system narrows the candidate pool. For instance, you might filter items based on user demographics, recent interactions, or popularity before applying a more complex model. The second stage uses a neural network or fine-grained ranking algorithm to reorder the smaller subset (e.g., 1,000 items) for precision. This hybrid approach balances speed and accuracy: Netflix, for example, uses coarse filtering followed by deep learning models to handle its vast catalog. Additionally, embedding-based retrieval (e.g., using Word2Vec or BERT) can cluster items into semantic groups, allowing you to precompute recommendations offline and serve them via cached lookups.

Finally, distributed computing and sharding are critical for scalability. Splitting the catalog across servers (sharding) lets you parallelize operations like model inference or similarity searches. Tools like Apache Spark or distributed databases (e.g., Cassandra) help manage partitioned data. For real-time updates, incremental training techniques—such as updating embeddings via online learning—avoid recomputing the entire model. For example, an e-commerce platform might update item embeddings hourly using new user clicks while keeping the ANN index updated. By combining these methods—ANN for fast retrieval, two-stage processing, and distributed infrastructure—developers can handle large catalogs efficiently while maintaining responsiveness.