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

RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models