Calibração de Sistemas de Recomendação com LLMs: Otimização de Prompts para Balancear Precisão, Diversidade e Justiça - Prenassi, Gabriel; Souza, Rodrigo; Sekido, Henrique; Fonseca, Guilherme; Manzato, Marcelo G.; Rocha, Leonardo (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2025)
Recommender Systems (RSs) play a central role in digital platforms, aiming to deliver relevant and personalized content. However, issues like popularity bias persist, limiting diversity and fairness. Calibration techniques seek to align recommendations with user preferences, often through post-processing adjustments. With the advent of Large Language Models (LLMs) such as GPT and LLaMA, new opportunities have emerged to personalize recommendations using prompt engineering. While recent approaches like prompt optimization have shown improvements in ranking accuracy, they often neglect other key aspects such as diversity, coverage, and fairness. This study investigates the use of LLMs for recommendation calibration, comparing their performance against traditional methods. We also evaluate the impact of different prompt optimization strategies across multiple metrics, including MAP, NDCG@10, MRMC, LTC, and GAP. Additionally, we employ a multicriteria utility function (MAUT) to analyze trade-offs between accuracy and diversity. Our results highlight the potential of LLMs and prompt engineering to enhance both personalization and fairness in Rss.