Role-Factorized Semantic IDs: Diagnosing and Mitigating Information Loss in Generative Recommendation

Recent advances in generative modeling have led to growing interest in generative recommendation. Central to this paradigm is the semantic ID (SID), a short sequence of discrete tokens representing an item. Existing methods often construct SIDs by fusing content and behavior into a single continuous representation before quantization. However, our preliminary study shows that this early-fusion strategy can lead to a representational compromise, where the learned item geometry is less aligned with the relational structures of both sources. To address this issue, we propose Role-Factorized Semantic IDs (RF-SID), which assigns content and behavior to separate coordinate blocks within a unified SID. This design represents both relations without requiring every discrete coordinate to reconcile them. Experiments on three public benchmarks show that RF-SID consistently outperforms state-of-the-art recommendation methods.