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The aim is to enhance sequential recommendation systems by addressing behavioral alignment issues in large language models.

SBT-Rec: A Structured Behavioral Tokenization Framework for LLM-Based Sequential Recommendation

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The aim is to enhance sequential recommendation systems by addressing behavioral alignment issues in large language models.
Developed a hierarchical discrete structure discovery module using recursive residual quantization.
Created a multi-scale behavioral semantic reconstruction module to reduce data noise.
Introduced a residual-aware modality distribution aligner for compatible input token transformation.
Proposed a single-stage behavioral-semantic adaptive optimization strategy for efficient fine-tuning.
SBT-Rec shows improved recommendation accuracy compared to baseline models.
Significant enhancements in training efficiency are observed.
Demonstrated increased robustness against data noise.

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This framework improves recommendation accuracy in generative systems by optimizing user behavior alignment.

Cheng et al. (2026) studied this question.

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