Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation-native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field-Aware Masked Auto-Encoding (FAMAE), which learns predictive-sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix-conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.

翻译：基于语义ID（SID）的推荐是扩展序列推荐系统的一个有前景的范式，但现有方法大多遵循以语义为中心的流程：项目嵌入从基础模型学习，并使用通用量化方案进行离散化。这种设计与生成式推荐的目标存在偏差：语义嵌入与协同预测弱耦合，而通用量化在降低自回归建模的序列不确定性方面效率低下。为解决这些问题，我们提出了ReSID，一个推荐原生的、原则性的SID框架，它从信息保留和序列可预测性的角度重新思考表示学习和量化，而不依赖于大语言模型。ReSID包含两个组件：(i) 场感知掩码自编码（FAMAE），它从结构化特征中学习预测充分的项目表示；(ii) 全局对齐正交量化（GAOQ），它通过联合减少语义模糊性和前缀条件不确定性，生成紧凑且可预测的SID序列。理论分析和在十个数据集上的大量实验证明了ReSID的有效性。ReSID持续优于强大的序列推荐和基于SID的生成式基线模型，平均提升超过10%，同时将分词成本降低了高达122倍。代码可在 https://github.com/FuCongResearchSquad/ReSID 获取。