@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
%A Zhang, Zhiyang
%A She, Junda
%A Cai, Kuo
%A Chen, Bo
%A Wang, Shiyao
%A Luo, Xinchen
%A Luo, Qiang
%A Tang, Ruiming
%A Li, Han
%A Gai, Kun
%A Zhou, Guorui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-unleashing
%X 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.
%U https://aclanthology.org/2026.findings-acl.984/
%P 19659-19673
Markdown (Informal)
[Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers](https://aclanthology.org/2026.findings-acl.984/) (Zhang et al., Findings 2026)
ACL
- Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, and Guorui Zhou. 2026. Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19659–19673, San Diego, California, United States. Association for Computational Linguistics.