This research delves into the nuanced relationship between fairness and personality considerations in language model-based recommendation systems (LLMs). Through meticulous analysis, we explore the advantages and drawbacks of LLMs concerning user satisfaction and gather user suggestions for future LLM development. Utilizing advanced language models and specialized evaluation techniques, we propose insights into mitigating the negative effects of unfairness and enhancing user satisfaction. Our findings underscore the potential of LLMs in refining recommendation systems' efficacy while pinpointing areas for improvement. The study sheds light on the complex dynamics at play in LLM-powered recommendation systems, contributing valuable insights to the ongoing discourse in the field.
CCS Concepts: •Information systems → Recommender system.
KEYWORDS
Large Language Model, Recommender Systems, Personality, Fairness Evaluation
