Scale-aware Identifiers for Generative Recommendation

Generative recommendation predicts the next item by autoregressively generating its semantic identifier (SID), making SID construction critical to recommendation performance. Existing hierarchical SID methods commonly use residual quantization (RQ), where each level selects a codeword and directly subtracts it from the residual. This update ignores the projection magnitude along the selected codeword and can produce non-orthogonal, non-minimum-norm residuals, leading to redundant decomposition across levels. Therefore, we propose Scale-aware Residual Quantization (SRQ), which computes projection magnitudes and subtracts the selected codeword with its corresponding scale, yielding an orthogonal, minimum-norm residual. To use the continuous magnitudes for autoregressive generation, we discretize them into quantile-based magnitude tokens. Together with codeword tokens, they form Scale-aware Identifiers for Generative Recommendation (SIGER). Moreover, SIGER can be integrated into two-stage training, unified tokenizer–recommender training, and LLM-based post-training. Analysis shows that SIGER improves residual orthogonality and codebook utilization, reducing SID collision from 5.26% to 2.99% with codeword tokens alone and to 0.04% with 128 magnitude bin size. These identifier-level improvements translate into stronger recommendation performance: SIGER improves two-stage baselines by up to 9.6% Recall@5 and 9.9% NDCG@5, improves over unified baselines by up to 2.4% NDCG@5 on Beauty and 8.0% Recall@10 on Pet, and achieves the best performance on the Office in the LLM-based setting. Further ablations on 2-level and code-only variants show that SIGER encodes semantic information more compactly and demonstrate the effectiveness of scale-aware decomposition.