CRAB: Codebook Rebalancing for Bias Mitigation in Generative Recommendation
Generative recommendation (GeneRec) has introduced a new paradigm that represents items as discrete semantic tokens and predicts items in a generative manner. Despite its strong performance across multiple recommendation tasks, existing GeneRec approaches still suffer from severe popularity bias and may even exacerbate it. In this work, we conduct a comprehensive empirical analysis to uncover the root causes of this phenomenon, yielding two core insights: 1) imbalanced tokenization inherits and can further amplify popularity bias from historical item interactions; 2) current training procedures disproportionately favor popular tokens while neglecting semantic relationships among tokens, t...
| Authors | Zezhong Fan, Ziheng Chen, Luyi Ma et al. |
|---|---|
| Primary category | cs.IR |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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