Paper
Fine-grained Semantics Integration for Large Language Model-based Recommendation
Authors
Jiawen Feng, Xiaoyu Kong, Leheng Sheng, Bin Wu, Chao Yi, Feifang Yang, Xiang-Rong Sheng, Han Zhu, Xiang Wang, Jiancan Wu, Xiangnan He
Abstract
Recent advances in Large Language Models (LLMs) have shifted in recommendation systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommender autoregressively generates sequences of semantic identifiers (SIDs) for target items conditioned on historical interaction. While prevalent LLM-based recommenders have demonstrated performance gains by aligning pretrained LLMs between the language space and the SID space, modeling the SID space still faces two fundamental challenges: (1) Semantically Meaningless Initialization: SID tokens are randomly initialized, severing the semantic linkage between the SID space and the pretrained language space at start point, and (2) Coarse-grained Alignment: existing SFT-based alignment tasks primarily focus on item-level optimization, while overlooking the semantics of individual tokens within SID sequences.To address these challenges, we propose TS-Rec, which can integrate Token-level Semantics into LLM-based Recommenders. Specifically, TS-Rec comprises two key components: (1) Semantic-Aware embedding Initialization (SA-Init), which initializes SID token embeddings by applying mean pooling to the pretrained embeddings of keywords extracted by a teacher model; and (2) Token-level Semantic Alignment (TS-Align), which aligns individual tokens within the SID sequence with the shared semantics of the corresponding item clusters. Extensive experiments on two real-world benchmarks demonstrate that TS-Rec consistently outperforms traditional and generative baselines across all standard metrics. The results demonstrate that integrating fine-grained semantic information significantly enhances the performance of LLM-based generative recommenders.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.22632v1</id>\n <title>Fine-grained Semantics Integration for Large Language Model-based Recommendation</title>\n <updated>2026-02-26T05:17:24Z</updated>\n <link href='https://arxiv.org/abs/2602.22632v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.22632v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Recent advances in Large Language Models (LLMs) have shifted in recommendation systems from the discriminative paradigm to the LLM-based generative paradigm, where the recommender autoregressively generates sequences of semantic identifiers (SIDs) for target items conditioned on historical interaction. While prevalent LLM-based recommenders have demonstrated performance gains by aligning pretrained LLMs between the language space and the SID space, modeling the SID space still faces two fundamental challenges: (1) Semantically Meaningless Initialization: SID tokens are randomly initialized, severing the semantic linkage between the SID space and the pretrained language space at start point, and (2) Coarse-grained Alignment: existing SFT-based alignment tasks primarily focus on item-level optimization, while overlooking the semantics of individual tokens within SID sequences.To address these challenges, we propose TS-Rec, which can integrate Token-level Semantics into LLM-based Recommenders. Specifically, TS-Rec comprises two key components: (1) Semantic-Aware embedding Initialization (SA-Init), which initializes SID token embeddings by applying mean pooling to the pretrained embeddings of keywords extracted by a teacher model; and (2) Token-level Semantic Alignment (TS-Align), which aligns individual tokens within the SID sequence with the shared semantics of the corresponding item clusters. Extensive experiments on two real-world benchmarks demonstrate that TS-Rec consistently outperforms traditional and generative baselines across all standard metrics. The results demonstrate that integrating fine-grained semantic information significantly enhances the performance of LLM-based generative recommenders.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.IR'/>\n <published>2026-02-26T05:17:24Z</published>\n <arxiv:primary_category term='cs.IR'/>\n <author>\n <name>Jiawen Feng</name>\n </author>\n <author>\n <name>Xiaoyu Kong</name>\n </author>\n <author>\n <name>Leheng Sheng</name>\n </author>\n <author>\n <name>Bin Wu</name>\n </author>\n <author>\n <name>Chao Yi</name>\n </author>\n <author>\n <name>Feifang Yang</name>\n </author>\n <author>\n <name>Xiang-Rong Sheng</name>\n </author>\n <author>\n <name>Han Zhu</name>\n </author>\n <author>\n <name>Xiang Wang</name>\n </author>\n <author>\n <name>Jiancan Wu</name>\n </author>\n <author>\n <name>Xiangnan He</name>\n </author>\n </entry>"
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