Semantic ID-based generative recommendation represents items as sequences of discrete tokens, but it inherently faces a trade-off between representational expressiveness and computational efficiency. Residual Quantization (RQ)-based approaches restrict semantic IDs to be short to enable tractable sequential modeling, while Optimized Product Quantization (OPQ)-based methods compress long semantic IDs through naive rigid aggregation, inevitably discarding fine-grained semantic information. To resolve this dilemma, we propose ACERec, a novel framework that decouples the granularity gap between fine-grained tokenization and efficient sequential modeling. It employs an Attentive Token Merger to distill long expressive semantic tokens into compact latents and introduces a dedicated Intent Token serving as a dynamic prediction anchor. To capture cohesive user intents, we guide the learning process via a dual-granularity objective, harmonizing fine-grained token prediction with global item-level semantic alignment. Extensive experiments on six real-world benchmarks demonstrate that ACERec consistently outperforms state-of-the-art baselines, achieving an average improvement of 14.40\% in NDCG@10, effectively reconciling semantic expressiveness and computational efficiency.

翻译：基于语义ID的生成式推荐将物品表示为离散标记序列，但本质上需要在表征表达能力与计算效率之间进行权衡。基于残差量化（RQ）的方法限制语义ID的长度以实现可处理的序列建模，而基于优化乘积量化（OPQ）的方法通过简单的刚性聚合压缩长语义ID，不可避免地丢弃细粒度语义信息。为解决这一困境，我们提出ACERec——一种新颖的框架，旨在解耦细粒度标记化与高效序列建模之间的粒度鸿沟。该框架采用注意力标记融合器将长表达性语义标记提炼为紧凑的潜在表示，并引入专用的意图标记作为动态预测锚点。为捕捉连贯的用户意图，我们通过双粒度目标指导学习过程，协调细粒度标记预测与全局物品级语义对齐。在六个真实世界基准数据集上的大量实验表明，ACERec始终优于最先进的基线方法，在NDCG@10指标上平均提升14.40%，有效实现了语义表达能力与计算效率的协同优化。