Fine-grained Semantics Integration for Large Language Model-based Recommendation

Abstract

Recent advances in Large Language Models (LLMs) have driven a shift in recommender 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: (i) Semantically Meaningless Initialization: SID tokens are initialized by random sampling without incorporating token-specific semantics, leaving them without explicit semantic correspondence to the pretrained language space; and (ii) 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 three real-world benchmarks demonstrate that TS-Rec consistently outperforms traditional and generative baselines across all standard metrics. These findings confirm the effectiveness of incorporating fine-grained semantic information into LLM-based generative recommenders. The implementation is available at https://anonymous.4open.science/r/TS-Rec-Review-8F99/.