Generative Sequential Recommendation (GSR) has emerged as a promising paradigm, reframing recommendation as an autoregressive sequence generation task over discrete Semantic IDs (SIDs), typically derived via codebook-based quantization. Despite its great potential in unifying retrieval and ranking, existing GSR frameworks still face two critical limitations: (1) impure and unstable semantic tokenization, where quantization methods struggle with interaction noise and codebook collapse, resulting in SIDs with ambiguous discrimination; and (2) lossy and weakly structured generation, where reliance solely on coarse-grained discrete tokens inevitably introduces information loss and neglects items' hierarchical logic. To address these issues, we propose a novel generative recommendation framework, PRISM, with Purified Representation and Integrated Semantic Modeling. Specifically, to ensure high-quality tokenization, we design a Purified Semantic Quantizer that constructs a robust codebook via adaptive collaborative denoising and hierarchical semantic anchoring mechanisms. To compensate for information loss during quantization, we further propose an Integrated Semantic Recommender, which incorporates a dynamic semantic integration mechanism to integrate fine-grained semantics and enforces logical validity through a semantic structure alignment objective. PRISM consistently outperforms state-of-the-art baselines across four real-world datasets, demonstrating substantial performance gains, particularly in high-sparsity scenarios.

翻译：生成式序列推荐作为一种新兴范式，通过将推荐任务重构为基于离散语义ID的自回归序列生成任务，展现出广阔前景。语义ID通常通过基于码书的量化方法生成。尽管该范式在统一检索与排序方面潜力巨大，现有生成式序列推荐框架仍面临两个关键局限：(1) 语义标记化的不纯净与不稳定性——量化方法难以处理交互噪声与码书坍缩问题，导致生成的语义ID缺乏明确区分度；(2) 生成过程的损耗性与弱结构性——仅依赖粗粒度离散标记不可避免地引入信息损失，且忽视了物品的层次化逻辑关系。为解决这些问题，我们提出一种新型生成式推荐框架PRISM，具备净化表示与集成语义建模能力。具体而言，为保障高质量标记化，我们设计了净化语义量化器，通过自适应协同去噪与层次化语义锚定机制构建鲁棒码书。为补偿量化过程中的信息损失，我们进一步提出集成语义推荐器，采用动态语义集成机制融合细粒度语义，并通过语义结构对齐目标强化逻辑有效性。在四个真实世界数据集上的实验表明，PRISM始终优于最先进的基线模型，尤其在高度稀疏场景下展现出显著的性能提升。