AI 中文总结
研究基于语义ID的生成式推荐中潜在推理的步骤分配问题，通过位置信息增益提出IBA框架，将潜在细化步骤视为有限资源，学习在语义ID位置间合理分配，实验证明该框架能提升推荐准确性并优化计算权衡。
AI 中文摘要
基于语义ID的生成式推荐通过生成短语义ID令牌序列来预测项目，每个令牌自回归生成。最近引入了潜在推理，通过在每个令牌决策前进行额外的隐藏状态计算来改进这一过程。这引发一个实际问题：当一个项目由语义ID令牌序列表示时，每个令牌应接收相同固定数量的潜在细化步骤，还是应在各位置更有效地分配这些步骤？我们通过位置信息增益（IG）研究此问题，它衡量每个语义ID位置降低目标项目不确定性的程度。我们观察到较早的语义ID位置通常提供更高信息增益，而较晚位置贡献的额外信息较少。我们进一步分析，对高IG位置应用更多细化往往带来更大预期收益。基于此观察，我们提出IBA，一种基于语义ID的生成式推荐的信息增益预算分配框架？IBA将潜在细化步骤视为有限计算资源，并学习如何在语义ID位置间分配它们，为信息丰富的位置分配更多细化，为贡献较小的位置分配较少细化。在多个公共数据集上的实验表明，IBA持续改进强大的生成式推荐基线，比固定或匹配不佳的步骤分配实现更好的准确性-计算权衡。
英文摘要
Semantic ID-based generative recommendation predicts an item by generating a short sequence of semantic ID tokens, where each token is produced autoregressively. Latent reasoning has recently been introduced to improve this process through additional hidden-state computation before each token decision. This raises a practical question: when one item is represented by a sequence of semantic ID tokens, should each token receive the same fixed number of latent refinement steps, or should these steps be allocated more effectively across positions? We study this question through position-wise information-gain (IG), which measures how much each semantic ID position reduces the uncertainty of the target item. We observe that earlier semantic ID positions usually provide higher information-gain, while later positions contribute less additional information. We further analyze that applying more refinement to high-IG positions tends to bring larger expected benefits. Based on this observation, we propose IBA, an Information-Gain Budget Allocation framework for semantic ID-based generative recommendation. IBA treats latent refinement steps as a limited computational resource and learns how to allocate them across semantic ID positions, assigning more refinement to informative positions and less to positions with smaller contribution. Experiments on multiple public datasets show that IBA consistently improves strong generative recommendation baselines and achieves a better accuracy--computation trade-off than fixed or poorly matched step allocations.
Comments12 pages, 7 figures, 5 tables
