FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation
Semantic ID (SID)-based generative recommendation has recently achieved remarkable success. However, existing methods suffer from a previously overlooked fairness issue, which we term \textbf{Token Frequency Bias}, where high-frequency SID tokens are systematically over-predicted while low-frequency SID tokens are under-predicted. This bias originates from the combined effects of imbalanced semantic codebooks during SID construction, and popularity bias together with the maximum likelihood estimation objective during recommendation training, resulting in unfair exposure across item categories. Existing SID methods mainly focus on improving codebook quality and overlook the impact of token...
| Authors | Yuchen Zheng, Sihan Xu, Jingwen Yang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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