A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models
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Rethinking Fairness in LLM-Based Recommender Systems: A Survey
A survey that organizes fairness research in LLM-based recommender systems via a two-dimensional taxonomy of bias mechanisms and fairness targets while linking to other trustworthy AI concerns.