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This paper introduces TopoGR, a generative recommendation framework that leverages a topology-preserving approach to enhance the representation of semantic IDs (SIDs) in recommendation systems. By recognizing the structural mismatch between the tokenization and generation processes, TopoGR employs Bit-decomposable Semantic IDs to maintain Hamming proximity, allowing for a more nuanced understanding of item relatedness beyond mere SID overlap. Experimental results across four benchmark datasets demonstrate that TopoGR significantly outperforms existing state-of-the-art methods in recommendation accuracy.
TopoGR reveals that preserving the topology of semantic IDs can dramatically enhance item relatedness in generative recommendation systems, leading to superior performance.
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.
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