@inproceedings{zhou-etal-2025-recbase,
    title = "{R}ec{B}ase: Generative Foundation Model Pretraining for Zero-Shot Recommendation",
    author = "Zhou, Sashuai  and
      Gan, Weinan  and
      Liu, Qijiong  and
      Lei, Ke  and
      Zhu, Jieming  and
      Huang, Hai  and
      Xia, Yan  and
      Tang, Ruiming  and
      Dong, Zhenhua  and
      Zhao, Zhou",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.786/",
    pages = "15598--15610",
    ISBN = "979-8-89176-332-6",
    abstract = "Recent advances in LLM-based recommendation have shown promise, yet their cross-domain generalization is hindered by a fundamental mismatch between language-centric pretraining and the recommendation task. Existing methods, relying on language-level knowledge, fail to capture dynamic, item-level user interests across domains. To bridge this gap, we propose RecBase, a domain-agnostic foundational model pretrained with a recommendation-oriented objective. RecBase leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross-domain generalization. To further align item semantics across domains, we introduce a unified item tokenizer that encodes items into hierarchical concept identifiers, enabling structured representation and efficient vocabulary sharing. The model is trained using an autoregressive objective to capture complex item-level sequential patterns. On eight real-world datasets, our 1.5B-parameter model matches or surpasses the performance of LLM baselines up to 7B parameters in zero-shot and cross-domain recommendation tasks."
}