Unleash the Potential of Long Semantic IDs for Generative Recommendation

TL;DR

ACERec introduces a novel framework for generative recommendation that balances semantic expressiveness and efficiency by distilling long semantic tokens into compact representations and aligning user intents.

Contribution

It proposes ACERec, which uses an Attentive Token Merger and Intent Token to effectively handle long semantic IDs for improved recommendation performance.

Findings

Outperforms state-of-the-art baselines on six benchmarks.

Achieves an average of 14.40% improvement in NDCG@10.

Effectively balances semantic richness and computational efficiency.

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.