动态分配推荐标识的语义容量，提升长ID生成效率与精度
Adaptive Semantic Capacity Allocation for Parallel Generative Recommendation
- 根据语义子空间需求动态分配编码容量，避免固定结构浪费
- 在相同资源下实现更优推荐精度，且保持一步并行生成
- 适合需要长标识符的推荐系统，尤其关注高效生成场景
自回归语义标识推荐模型受限于昂贵的束搜索解码，难以生成长标识符。并行生成方法虽可同时预测所有语义令牌，缓解该瓶颈，但现有方法仍依赖人工预设的同质化标识结构，各语义槽数量和代码本大小均为固定超参数，忽视不同语义子空间的异质容量需求，可能导致对低效槽位的冗余容量分配。实验表明，均匀扩展语义槽带来的增益有限，说明同质化结构存在冗余。本文提出InforID，一种轻量级自适应语义目标构建框架，将固定容量预算分配至候选语义槽，联合决定有效标识长度与各槽代码本大小。实验显示，在相当容量预算下，推荐精度显著提升，同时保持单步并行预测能力。
原文摘要 · Abstract (English)
Autoregressive semantic ID recommenders are constrained by expensive beam-search decoding, which limits the practical length of item identifiers. Parallel generation methods alleviate this bottleneck by predicting all semantic ID tokens simultaneously, enabling longer IDs. However, existing semantic ID methods still rely on manually predefined and homogeneous ID structures, where both the number of semantic slots and the codebook size of each slot are treated as fixed hyperparameters. This ignores the heterogeneous capacity demands of different semantic subspaces and may allocate prediction capacity to slots with limited utility. We show that uniformly expanding semantic slots can provide limited gains, indicating redundant capacity in homogeneous semantic IDs. We propose InforID, a lightweight adaptive semantic target construction framework for parallel generative recommendation. InforID allocates a fixed capacity budget across candidate semantic slots, thereby jointly determining the effective ID length and slot-specific codebook sizes. Experiments demonstrate improved recommendation accuracy under comparable capacity budgets while preserving one-step parallel prediction.
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