@inproceedings{zhang-etal-2026-unleashing,
    title = "Unleashing the Native Recommendation Potential: {LLM}-Based Generative Recommendation via Structured Term Identifiers",
    author = "Zhang, Zhiyang  and
      She, Junda  and
      Cai, Kuo  and
      Chen, Bo  and
      Wang, Shiyao  and
      Luo, Xinchen  and
      Luo, Qiang  and
      Tang, Ruiming  and
      Li, Han  and
      Gai, Kun  and
      Zhou, Guorui",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.984/",
    pages = "19659--19673",
    ISBN = "979-8-89176-395-1",
    abstract = "Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerged as a pivotal research direction in generative recommendation. However, existing methods face bottlenecks in constructing item identifiers. Text-based methods introduce LLMs' vast output space, leading to hallucination, while methods based on Semantic IDs (SIDs) encounter a semantic gap between SIDs and LLMs' native vocabulary, requiring costly vocabulary expansion and alignment training. To address this, this paper introduces Term IDs (TIDs), defined as a set of semantically rich and standardized textual keywords, to serve as robust item identifiers. We propose GRAM, a novel framework centered on TIDs, employs Context-aware Term Generation to convert item{'}s metadata into standardized TIDs and utilizes Integrative Instruction Fine-tuning to collaboratively optimize term internalization and sequential recommendation. Additionally, Elastic Identifier Grounding is designed for robust item mapping. Extensive experiments on real-world datasets demonstrate that GRAM significantly outperforms baselines across multiple scenarios, pointing a promising direction for generalizable and high-performance generative recommendation systems."
}