We explore a novel use case for Large Language Models (LLMs) in recommendation: generating natural language user taste profiles from listening histories. Unlike traditional opaque embeddings, these profiles are interpretable, editable, and give users greater transparency and control over their personalization. However, it is unclear whether users actually recognize themselves in these profiles, and whether […] The post Biases in LLM-Generated Musical Taste Profiles for Recommendation appear...
