This study explores the nuanced capabilities and inherent biases of Recommender Systems using Large Language Models (RecLLMs), with a focus on ChatGPT-based systems. It studies into the contrasting behaviors of generative models and traditional collaborative filtering models in movie recommendations. The research primarily investigates prompt design strategies and their impact on various aspects of recommendation quality, including accuracy, provider fairness, diversity, stability, genre dominance, and temporal freshness (recency). Our experimental analysis reveals that the introduction of specific 'system roles' and 'prompt strategies' in RecLLMs significantly influences their performance. For instance, role-based prompts enhance fairness and diversity in recommendations, mitigating popularity bias. We find that while GPT-based models do not always match the performance of CF baselines, they exhibit a unique tendency to recommend newer and more diverse movie genres. Notably, GPT-based models tend to recommend more recent films, particularly those released post-2000, and show a preference for genres like \sq{Drama} and Comedy, and Romance (compared to CF Action, Adventure) presumably due to the RecLLMs' training on varied data sets, which allows them to capture recent trends and discussions more effectively than CF models. Interestingly, our results demonstrate that the 'Simple' and 'Chain of Thought (COT)' paradigms yield the highest accuracy. These findings imply the potential of combining these strategies with scenarios that favor more recent content, thereby offering a more balanced and up-to-date recommendation experience. This study contributes significantly to the understanding of emerging RecLLMs, particularly in the context of harms and biases within these systems.

这项研究通过使用大型语言模型（RecLLMs）来探索推荐系统的复杂能力和内在偏见，重点研究基于ChatGPT的系统。研究了生成模型和传统协同过滤模型在电影推荐中的不同行为。实验分析表明，在RecLLMs中引入特定的“系统角色”和“提示策略”显著影响它们的表现。这些发现揭示了将这些策略与偏好更近期内容的情景相结合的潜力，从而提供更平衡和最新的推荐体验。

ChatGPT 基于推荐系统中的偏见理解：供应者公平性、时间稳定性和最新性