11.6. Chapter Summary

• A recommender system is underpinned by a complex architecture that incorporates a multitude of system components such as message queues, feature stores, neural networks, embedding tables, parameter servers, training servers, and inference servers.

• A recommendation decision typically proceeds through a pipeline that includes both the retrieval and ranking stages. The ranking stage can be further dissected into pre-ranking, ranking, and post-ranking.

• To ensure high-quality recommendations, a recommendation model requires continual updates. Generally, the more frequent the model updates, the higher the quality of the recommendations.

• Modern recommender systems are delving into the possibilities of real-time machine learning. To make this concept practically feasible, researchers are exploring how to leverage the unique data characteristics of recommender systems to address several critical system challenges. This exploration has led to new system designs that include application-specific synchronization protocols, application-aware network update scheduling, and online model state management.