Unleash the Potential of Long Semantic IDs for Generative Recommendation

• URL: http://arxiv.org/abs/2602.13573v1
• Date: Sat, 14 Feb 2026 03:15:31 GMT
• Title: Unleash the Potential of Long Semantic IDs for Generative Recommendation
• Authors: Ming Xia, Zhiqin Zhou, Guoxin Ma, Dongmin Huang,
• Abstract summary: ACERec is a novel framework that decouples the gap between fine-grained tokenization and efficient sequential modeling.<n>It consistently outperforms state-of-the-art baselines on six real-world benchmarks.
• Score: 5.6264583086973685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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