LLMFOSA improves recommendation fairness by using multi-persona LLMs to infer and then remove sensitive attribute information from user embeddings, without using true sensitive labels during training.
Club: A contrastive log-ratio upper bound of mutual information
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Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs
LLMFOSA improves recommendation fairness by using multi-persona LLMs to infer and then remove sensitive attribute information from user embeddings, without using true sensitive labels during training.