发表机构
Huawei Ireland Research Centre（华为爱尔兰研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
将语义ID构建重构为递归图聚类，提出统一框架GrIS，通过可配置的图构建与划分轴，在多个数据集上最高提升Hit@10达52%。
AI 中文摘要
现有的生成式推荐中的语义ID（SIDs）工作将SID构建视为一个表示学习问题：将物品编码到量化潜在空间并读取码字。我们认为这一观点是偶然的。SID构建本质上是一个递归聚类问题，一旦以这种方式表述，自然的聚类对象是一个图，其节点携带语义内容，边携带协同信号；SID分配便成为层次化图划分。这种重新框架产生了一个统一框架——图感知语义ID（GrIS），它包含而非取代先前的方法。RQ-VAE和RQ-KMeans被恢复为图为空时的特例，从而将仅内容量化暴露为更大设计空间中的一个角落，该空间沿两个迄今被压缩的轴展开：图构建和递归划分算法。我们探索了两个对比实例：RecDMoN，通过可微图池化执行层次化分配；以及RQ-GAE，它通过图感知物品表示和图重构目标扩展了RQ-VAE。在多个真实世界数据集上，GrIS持续优于协同过滤感知的最先进方法，最高提升达+52%的Hit@10。由于图构建和划分是显式且可分别配置的组件，任一轴上的改进都可以系统地组合和评估。
英文摘要
Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent space and read off codes. We argue this view is incidental. SID construction is, at heart, a recursive clustering problem, and once stated this way the natural object to cluster is a graph whose nodes carry semantic content and whose edges carry collaborative signal; SID assignment becomes a hierarchical graph partition. This reframing yields a unified framework, Graph-Informed Semantic IDs (GrIS), that subsumes prior approaches rather than displacing them. RQ-VAE and RQ-KMeans are recovered as the special case where the graph is empty, exposing content-only quantisation as one corner of a larger design space along two so-far-collapsed axes: graph construction and recursive partition algorithm. We explore two contrasting instantiations: RecDMoN, which performs hierarchical assignment via differentiable graph pooling, and RQ-GAE, which extends RQ-VAE with graph-aware item representations and a graph reconstruction objective. On multiple real-world datasets, GrIS consistently improves over CF-aware SOTA, with gains of up to +52\% Hit@10. Because graph construction and partition are explicit, separately configurable components, improvements on either axis can be combined and evaluated systematically.
