# Recommendation Systems

In the context of embeddings, recommendation systems use vector representations to capture similarities between items, such as products or content. By converting items and user preferences into embeddings, these systems can measure how closely related different items are based on vector proximity, allowing them to recommend similar products or content based on a user's past interactions. This approach improves recommendation accuracy and efficiency by enabling meaningful, scalable comparisons of complex data.

Learn more from the following resources:

- [@article@What role does AI play in recommendation systems and engines?](https://www.algolia.com/blog/ai/what-role-does-ai-play-in-recommendation-systems-and-engines/)
- [@article@What is a recommendation engine?](https://www.ibm.com/think/topics/recommendation-engine)