发表机构
University of the Chinese Academy of Sciences; Institute of Automation, Chinese Academy of Sciences（中国科学院大学; 中国科学院自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有并行生成式推荐中语义ID结构固定的问题，提出自适应语义目标构建框架InforID，通过分配容量预算确定ID长度与码本大小，提升了推荐准确率。
AI 中文摘要
自回归语义ID推荐器受限于代价高昂的集束搜索解码，这限制了物品标识符的实际长度。并行生成方法通过同时预测所有语义ID令牌缓解了这一瓶颈，从而支持更长的ID。然而，现有的语义ID方法仍依赖手动预定义的同质ID结构，其中语义槽数量和每个槽的码本大小均被视为固定超参数。这忽略了不同语义子空间的异质容量需求，可能将预测容量分配给效用有限的槽。我们表明，均匀扩展语义槽只能提供有限增益，表明同质语义ID存在冗余容量。我们提出InforID，一种用于并行生成式推荐的轻量型自适应语义目标构建框架。InforID在候选语义槽间分配固定容量预算，从而联合确定有效ID长度和槽特定码本大小。实验表明，在可比容量预算下，该方法在保持一步并行预测的同时，提升了推荐准确率。
英文摘要
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.
