Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness

Understand

Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior.

• However, they often inherit biases from skewed training data, favoring mainstream content while underrepresenting diverse or non-traditional options.
• This study explores the interplay between bias and LLM-based recommendation systems, focusing on music, song, and book recommendations across diverse demographic and cultural groups.
• This paper analyzes bias in LLM-based recommendation systems across multiple models (GPT, LLaMA, and Gemini), revealing its deep and pervasive impact on outcomes.