In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial. Large Language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability in news recommendation systems remains to be validated. This paper introduces RecPrompt, the first framework for news recommendation that leverages the capabilities of LLMs through prompt engineering. This system incorporates a prompt optimizer that applies an iterative bootstrapping process, enhancing the LLM-based recommender's ability to align news content with user preferences and interests more effectively. Moreover, this study offers insights into the effective use of LLMs in news recommendation, emphasizing both the advantages and the challenges of incorporating LLMs into recommendation systems.

翻译：在个性化新闻推荐这一不断发展的领域中，理解底层数据的语义至关重要。GPT-4等大语言模型在处理自然语言方面展现出卓越性能，但其在新闻推荐系统中的适用程度仍有待验证。本文提出RecPrompt——首个通过提示工程利用大语言模型能力的新闻推荐框架。该系统集成了提示优化器，采用迭代自举过程，使基于大语言模型的推荐器能够更有效地将新闻内容与用户偏好及兴趣对齐。此外，本研究深入探讨了大语言模型在新闻推荐中的有效应用，既强调了将大语言模型集成到推荐系统中的优势，也指出了所面临的挑战。