By pretraining on semantic item relationships and using hierarchical preference decomposition, generative recommendation models can better capture both what items are similar and what users actually want to buy or interact with.
REGER improves generative recommendation systems by explicitly modeling relationships between items through semantic IDs. It pretrains models to learn item similarities, uses hierarchical attention to capture user history patterns, and applies preference distillation to better predict which items users will interact with next.