Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers

• URL: http://arxiv.org/abs/2601.06798v1
• Date: Sun, 11 Jan 2026 07:53:20 GMT
• Title: Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
• Authors: Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou,
• Abstract summary: This paper introduces Term IDs (TIDs), defined as a set of semantically rich and standardized textual keywords, to serve as robust item identifiers.<n>We propose GRLM, a novel framework centered on TIDs, to convert item's metadata into standardized TIDs and utilize Integrative Instruction Fine-tuning to collaboratively optimize term internalization and sequential recommendation.
• Score: 51.64398574262054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 GRLM, 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 GRLM significantly outperforms baselines across multiple scenarios, pointing a promising direction for generalizable and high-performance generative recommendation systems.

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