How would you design a high-performance recommendation system backend in Java?

A recommendation system combines batch processing, real-time streams, and ranking engines.

User behavior data is ingested via Kafka, processed in streaming pipelines for real-time signals, and batch jobs compute embeddings or collaborative filtering models. A ranking service merges signals and returns results via low-latency APIs with caching.