Generative Recommendation (GR) has emerged as a transformative paradigm that reformulates the traditional cascade ranking system into a sequence-to-item generation task, facilitated by the use of discrete Semantic IDs (SIDs). However, current SIDs are suboptimal as the indexing objectives (Stage 1) are misaligned with the actual recommendation goals (Stage 2). Since these identifiers remain static (Stage 2), the backbone model lacks the flexibility to adapt them to the evolving complexities of user interactions. Furthermore, the prevailing strategy of flattening hierarchical SIDs into token sequences leads to sequence length inflation, resulting in prohibitive computational overhead and inference latency. To address these challenges, we propose IntRR, a novel framework that integrates objective-aligned SID Redistribution and structural Length Reduction. By leveraging item-specific Unique IDs (UIDs) as collaborative anchors, this approach dynamically redistributes semantic weights across hierarchical codebook layers. Concurrently, IntRR handles the SID hierarchy recursively, eliminating the need to flatten sequences. This ensures a fixed cost of one token per item. Extensive experiments on benchmark datasets demonstrate that IntRR yields substantial improvements over representative generative baselines, achieving superior performance in both recommendation accuracy and efficiency.

翻译：生成式推荐作为一种变革性范式，通过离散语义ID的使用，将传统级联排序系统重构为序列到物品的生成任务。然而，现有语义ID存在不足，因为索引目标（第一阶段）与实际推荐目标（第二阶段）存在错位。由于这些标识符保持静态（第二阶段），骨干模型缺乏灵活性以适应用户交互日益复杂的动态变化。此外，当前将层次化语义ID展平为令牌序列的主流策略会导致序列长度膨胀，进而产生难以承受的计算开销和推理延迟。为应对这些挑战，我们提出IntRR——一种集成目标对齐的语义ID重分布与结构化长度缩减的新框架。该方法通过利用物品特定唯一ID作为协同锚点，动态地在层次化码本层间重新分配语义权重。同时，IntRR以递归方式处理语义ID层次结构，无需展平序列，从而确保每个物品的固定成本仅为一个令牌。在基准数据集上的大量实验表明，IntRR相较于代表性生成式基线方法取得显著提升，在推荐准确性与效率方面均实现了优越性能。