LLMSQRec: A study on a large language model‐based framework for dual‐view semantic‐quantized recommendation

Abstract Sequential recommendation is widely used in domains such as e‐commerce and social media but still suffers from data sparsity, long‐tail distributions, and popularity bias. Large language models (LLMs) provide rich semantics and transferability, yet their integration with collaborative signals remains challenging due to representation inconsistency, high prompting costs, weakened user modeling, and unresolved bias. To address these issues, we propose LLMSQRec, a unified framework that fuses LLM‐derived semantic features with ID embeddings via dual‐view modeling. Specifically, representation inconsistency is alleviated through semantic‐collaborative alignment; prompting costs are reduced by combining frozen LLM embeddings with lightweight vector quantization; user modeling is strengthened by jointly encoding semantic and collaborative signals, and popularity bias is mitigated through curriculum learning with popularity‐aware regularization. Experiments on multiple real‐world datasets demonstrate that LLMSQRec significantly outperforms existing methods in sparse and long‐tail settings, achieving superior accuracy, robustness, and diversity.