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Evidence record 19189 · automatically gathered

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 estim

Record details

Published: 13 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 August 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (13 August 2026), “FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation,” evidence record 19189, https://ethics.ai/record/19189 (originally published by arXiv).

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