Generative recommender (GR) systems are emerging as a powerful new approach for large-scale personalization. Instead of treating recommendation as a set of isolated retrieval, ranking, and prediction stages, GRs reformulate recommendation as sequence modeling over user behavior. A user’s interactions, context, candidate items, and actions become tokens in a high-cardinality event stream, and the model learns to generate or score the next relevant items from that sequence.

This approach is especially attractive for modern recommendation workloads, where user histories can be long, item catalogs are constantly changing, and personalization quality depends on modeling rich sequential behavior.…