CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation

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Generative recommendation models next-item prediction as autoregressive generation over tokenized user histories, where each item is represented as a sequence of discrete tokens. However, existing methods typically construct these tokens by compressing heterogeneous item attributes, such as ID, category, title, and description, into a single latent representation before quantization, which obscures the hierarchical structure of item semantics and limits their ability to capture how user preferences evolve from broad interests to specific choices during web interactions. To address this issue, we propose CoFiRec, a generative recommendation framework that explicitly incorporates Coarse-to-Fine semantic structure into both item tokenization and decoding. Specifically, CoFiRec organizes item information into multiple semantic levels, spanning high-level categories, fine-grained textual content, and collaborative signals. Building on this design, we introduce the CoFiRec Tokenizer, which tokenizes each semantic level independently while preserving their structural order, thereby better reflecting how users refine their preferences and enabling more structured generation. During autoregressive decoding, the language model generates item tokens progressively from coarse to fine, allowing the recommendation process to better capture the natural refinement of user intent. Extensive experiments on multiple public benchmarks and backbone models demonstrate that CoFiRec consistently outperforms existing baselines, and our theoretical analysis further shows that structured hierarchical tokenization reduces the expected dissimilarity between generated items and ground-truth targets. Our code and datasets are available at https://github.com/YennNing/CoFiRec.