Semantic IDs (SIDs) are compact discrete representations derived from multimodal item features, serving as a unified abstraction for ID-based and generative recommendation. However, learning high-quality SIDs remains challenging due to two issues. (1) Collision problem: the quantized token space is prone to collisions, in which semantically distinct items are assigned identical or overly similar SID compositions, resulting in semantic entanglement. (2) Collision-signal heterogeneity: collisions are not uniformly harmful. Some reflect genuine conflicts between semantically unrelated items, while others stem from benign redundancy or systematic data effects. To address these challenges, we propose Qualification-Aware Semantic ID Learning (QuaSID), an end-to-end framework that learns collision-qualified SIDs by selectively repelling qualified conflict pairs and scaling the repulsion strength by collision severity. QuaSID consists of two mechanisms: Hamming-guided Margin Repulsion, which translates low-Hamming SID overlaps into explicit, severity-scaled geometric constraints on the encoder space; and Conflict-Aware Valid Pair Masking, which masks protocol-induced benign overlaps to denoise repulsion supervision. In addition, QuaSID incorporates a dual-tower contrastive objective to inject collaborative signals into tokenization. Experiments on public benchmarks and industrial data validate QuaSID. On public datasets, QuaSID consistently outperforms strong baselines, improving top-K ranking quality by 5.9% over the best baseline while increasing SID composition diversity. In an online A/B test on Kuaishou e-commerce with a 5% traffic split, QuaSID increases ranking GMV-S2 by 2.38% and improves completed orders on cold-start retrieval by up to 6.42%. Finally, we show that the proposed repulsion loss is plug-and-play and enhances a range of SID learning frameworks across datasets.

翻译：语义ID（SID）是从多模态物品特征中提取的紧凑离散表示，可作为基于ID和生成式推荐的统一抽象。然而，由于两个问题，学习高质量的SID仍然具有挑战性。(1) 冲突问题：量化后的标记空间容易发生冲突，即语义不同的物品被分配相同或过度相似的SID组合，导致语义纠缠。(2) 冲突信号异质性：冲突并非一律有害。有些反映了语义无关物品之间的真实冲突，而另一些则源于良性冗余或系统性数据效应。为应对这些挑战，我们提出了资格感知语义ID学习（QuaSID），这是一个端到端框架，通过有选择地排斥合格的冲突对，并根据冲突严重程度缩放排斥强度，来学习具备冲突资格判定的SID。QuaSID包含两种机制：汉明距离引导的边缘排斥，将低汉明距离的SID重叠转化为编码器空间上显式的、按严重程度缩放的几何约束；以及冲突感知的有效对掩码，用于掩蔽协议诱导的良性重叠以去除排斥监督中的噪声。此外，QuaSID结合了双塔对比学习目标，将协同信号注入到标记化过程中。在公共基准和工业数据上的实验验证了QuaSID的有效性。在公共数据集上，QuaSID始终优于强基线模型，在提高SID组合多样性的同时，将Top-K排序质量较最佳基线提升了5.9%。在快手电商平台上进行的在线A/B测试（5%流量分割）中，QuaSID使排序GMV-S2提升了2.38%，并将冷启动检索的完成订单量最高提升了6.42%。最后，我们证明了所提出的排斥损失具有即插即用特性，能够增强跨数据集的一系列SID学习框架。