  Modern recommender systems perform large-scale retrieval by first embedding
queries and item candidates in the same unified space, followed by approximate
nearest neighbor search to select top candidates given a query embedding. In
this paper, we propose a novel generative retrieval approach, where the
retrieval model autoregressively decodes the identifiers of the target
candidates. To that end, we create semantically meaningful tuple of codewords
to serve as a Semantic ID for each item. Given Semantic IDs for items in a user
session, a Transformer-based sequence-to-sequence model is trained to predict
the Semantic ID of the next item that the user will interact with. To the best
of our knowledge, this is the first Semantic ID-based generative model for
recommendation tasks. We show that recommender systems trained with the
proposed paradigm significantly outperform the current SOTA models on various
datasets. In addition, we show that incorporating Semantic IDs into the
sequence-to-sequence model enhances its ability to generalize, as evidenced by
the improved retrieval performance observed for items with no prior interaction
history.
