How do recommender systems handle the scalability issue?

Recommender systems handle the scalability issue through various techniques such as:

1. Matrix factorization: This technique reduces the dimensionality of the data by decomposing the user-item interaction matrix into lower-dimensional latent factors. It allows for efficient computation and storage of recommendations.

2. Parallel processing: Recommender systems can leverage parallel processing frameworks and distributed computing to handle large datasets. By distributing the computation across multiple machines, scalability can be achieved.

3. Incremental updates: Instead of recomputing recommendations from scratch every time new data is added, recommender systems can use incremental updates. This involves updating the recommendations based on the new data, which reduces the computational overhead and improves scalability.

4. Sampling techniques: Rather than processing the entire dataset, recommender systems can use sampling techniques to work with a subset of the data. This helps in reducing the computational complexity and allows for faster recommendation generation.

5. Caching and precomputation: Recommender systems can cache precomputed recommendations for frequently accessed items or popular user-item combinations. This reduces the need for recomputation and improves response time, especially for real-time recommendation scenarios.

Overall, these techniques help recommender systems handle the scalability issue by optimizing computation, reducing data dimensionality, and leveraging parallel processing and caching mechanisms.