发表机构
Xidian University; Alibaba; University of Science and Technology of China（西安电子科技大学; 阿里巴巴; 中国科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于语义ID的生成式推荐中忽视语义ID空间拓扑结构的问题，提出TopoGR框架，利用可位分解语义ID的汉明拓扑结构，在输入、监督、推理阶段提升性能，实验证明其优于现有基线。
AI 中文摘要
基于语义ID的生成式推荐将每个项目令牌化为离散语义ID序列并通过生成语义ID预测下一个项目。现有方法通常将语义ID视为独立离散符号，忽略了语义ID空间的拓扑结构。本文发现令牌化和生成之间存在结构不匹配问题。为此提出TopoGR，一个基于可位分解语义ID的拓扑保留生成式推荐框架。每个可位分解语义ID以位分解形式学习，可确定性转换为标准整数语义ID并暴露显式汉明几何结构。TopoGR在三个阶段利用此拓扑结构：在输入层保留汉明邻近性；注入拓扑感知监督；推理时进行汉明一致重排。实验表明TopoGR在推荐性能上优于现有基线。
英文摘要
Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs. However, existing methods typically regard SIDs as independent discrete symbols, while often overlooking the topology of the learned semantic ID space. We identify a structural mismatch between tokenization and generation: the tokenizer learns a structured code space with semantic neighborhood relations, whereas the generator consumes semantic ID tokens as independent categorical symbols. Consequently, item relatedness is reduced to exact semantic ID overlap, making it difficult to identify semantically similar items whose semantic IDs do not overlap. To address this issue, we propose TopoGR, a topology-preserving generative recommendation framework based on Bit-decomposable Semantic ID(Binary SID). Each Binary SID is learned in a bit-decomposable form and can be deterministically converted to a standard integer SID, while exposing an explicit Hamming geometry. TopoGR exploits this topology at three stages: binary SID features preserve Hamming proximity at the input layer; Hamming soft targets inject topology-aware supervision; and Hamming-consistent reranking aligns candidate items with the predicted binary prototype during inference. We further verify that the Hamming topology can capture item relatedness beyond exact SID matching. Experiments on four benchmark datasets show that TopoGR consistently outperforms existing state-of-the-art baselines in recommendation performance.
Commentsunder review
